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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-12-05
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">143</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">14</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.05033v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>套利：通过优势感知推测实现高效推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Arbitrage: Efficient Reasoning via Advantage-Aware Speculation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Monishwaran Maheswaran, Rishabh Tiwari, Yuezhou Hu, Kerem Dilmen, Coleman Hooper...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何提高大语言模型在推理任务中的计算效率。其核心方法是提出一个名为Arbitrage的步骤级推测生成框架，通过训练一个轻量级路由器动态预测目标模型何时可能产生显著更优的推理步骤，从而在语义验证层面实现更智能的生成路由，以优化效率与质量的权衡。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出一种基于优势感知推测的推理加速框架，直接针对LLM推理效率这一核心瓶颈，属于Transformer架构效率提升和LLM直接应用的关键技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:50:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05033v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05033v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivates techniques to improve the performance-cost ratio. Among these techniques, Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to autoregressively propose tokens, which are then verified in parallel by a more capable target model. However, due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding struggles in reasoning tasks. Although recent works have shifted to step-level semantic verification, which improve efficiency by accepting or rejecting entire reasoning steps, existing step-level methods still regenerate many rejected steps with little improvement, wasting valuable target compute. To address this challenge, we propose Arbitrage, a novel step-level speculative generation framework that routes generation dynamically based on the relative advantage between draft and target models. Instead of applying a fixed acceptance threshold, Arbitrage uses a lightweight router trained to predict when the target model is likely to produce a meaningfully better step. This routing approximates an ideal Arbitrage Oracle that always chooses the higher-quality step, achieving near-optimal efficiency-accuracy trade-offs. Across multiple mathematical reasoning benchmarks, Arbitrage consistently surpasses prior step-level Speculative Decoding baselines, reducing inference latency by up to $\sim2\times$ at matched accuracy.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04871v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>STELLA：基于语义抽象引导大语言模型进行时间序列预测
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junjie Fan, Hongye Zhao, Linduo Wei, Jiayu Rao, Guijia Li, Jiaxin Yuan, Wenqi Xu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何更有效地利用大型语言模型进行时间序列预测。其核心方法是提出STELLA框架，通过动态语义抽象机制将时间序列解耦为趋势、季节性和残差分量，并生成层次化语义锚点（全局语料级先验和细粒度行为提示）来引导LLM建模内在动态行为。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出STELLA框架，通过语义抽象引导LLM进行时间序列预测，直接应用LLM技术解决预测问题，属于直接LLM应用范畴，与用户关注点高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:56:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04871v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04871v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent adaptations of Large Language Models (LLMs) for time series forecasting often fail to effectively enhance information for raw series, leaving LLM reasoning capabilities underutilized. Existing prompting strategies rely on static correlations rather than generative interpretations of dynamic behavior, lacking critical global and instance-specific context. To address this, we propose STELLA (Semantic-Temporal Alignment with Language Abstractions), a framework that systematically mines and injects structured supplementary and complementary information. STELLA employs a dynamic semantic abstraction mechanism that decouples input series into trend, seasonality, and residual components. It then translates intrinsic behavioral features of these components into Hierarchical Semantic Anchors: a Corpus-level Semantic Prior (CSP) for global context and a Fine-grained Behavioral Prompt (FBP) for instance-level patterns. Using these anchors as prefix-prompts, STELLA guides the LLM to model intrinsic dynamics. Experiments on eight benchmark datasets demonstrate that STELLA outperforms state-of-the-art methods in long- and short-term forecasting, showing superior generalization in zero-shot and few-shot settings. Ablation studies further validate the effectiveness of our dynamically generated semantic anchors.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04810v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>EMMA：基于统一架构的高效多模态理解、生成与编辑
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin He, Longhui Wei, Jianbo Ouyang, Lingxi Xie, Qi Tian
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何构建高效统一的多模态理解、生成与编辑架构。其核心方法是：通过32倍压缩的自动编码器减少生成令牌数，采用通道级而非令牌级拼接来统一视觉理解与生成表示，并利用共享解耦网络和专家混合机制在任务间实现协同优化与感知能力提升。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出统一多模态架构EMMA，其高效压缩、通道级拼接和专家混合机制直接对应Transformer效率提升和异构数据处理需求，对推荐/搜索系统中的多模态内容理解与生成具有核心应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:01:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04810v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04810v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose EMMA, an efficient and unified architecture for multimodal understanding, generation and editing. Specifically, EMMA primarily consists of 1) An efficient autoencoder with a 32x compression ratio, which significantly reduces the number of tokens required for generation. This also ensures the training balance between understanding and generation tasks by applying the same compression ratio to images. 2) Channel-wise concatenation instead of token-wise concatenation among visual understanding and generation tokens, which further reduces the visual tokens in unified architectures. 3) A shared-and-decoupled network that enables mutual improvements across tasks while meeting the task-specific modeling requirements. 4) A mixture-of-experts mechanism adopted for visual understanding encoder, which substantially improves perceptual capabilities with a few parameters increase. Extensive experiments have shown that EMMA-4B can significantly outperform state-of-the-art unified multimodal approaches (e.g., BAGEL-7B) in both efficiency and performance, while also achieving competitive results compared to recent multimodal understanding and generation experts (e.g., Qwen3-VL and Qwen-Image). We believe that EMMA lays a solid foundation for the future development of unified multimodal architectures.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04343v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>个性化悖论：智能体AI问答中的语义损失与推理增益
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            The Personalization Paradox: Semantic Loss vs. Reasoning Gains in Agentic AI Q&A
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Satyajit Movidi, Stephen Russell
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究个性化在智能问答系统中如何影响不同维度的性能评估，核心发现是：个性化会提升推理质量和事实依据，但会因偏离通用参考文本而降低当前语义相似度指标的分数，这暴露了现有LLM评估方法不适用于评估用户特定响应的结构缺陷。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究个性化在AI问答系统（如推荐/搜索场景）中的核心矛盾，揭示了当前评估方法的缺陷，为个性化建模提供了方法论基础，与用户建模、系统评估等焦点高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 00:12:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04343v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04343v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    AIVisor, an agentic retrieval-augmented LLM for student advising, was used to examine how personalization affects system performance across multiple evaluation dimensions. Using twelve authentic advising questions intentionally designed to stress lexical precision, we compared ten personalized and non-personalized system configurations and analyzed outcomes with a Linear Mixed-Effects Model across lexical (BLEU, ROUGE-L), semantic (METEOR, BERTScore), and grounding (RAGAS) metrics. Results showed a consistent trade-off: personalization reliably improved reasoning quality and grounding, yet introduced a significant negative interaction on semantic similarity, driven not by poorer answers but by the limits of current metrics, which penalize meaningful personalized deviations from generic reference texts. This reveals a structural flaw in prevailing LLM evaluation methods, which are ill-suited for assessing user-specific responses. The fully integrated personalized configuration produced the highest overall gains, suggesting that personalization can enhance system effectiveness when evaluated with appropriate multidimensional metrics. Overall, the study demonstrates that personalization produces metric-dependent shifts rather than uniform improvements and provides a methodological foundation for more transparent and robust personalization in agentic AI.
                </div>
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.05105v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>语义软引导：无需强化学习的LLM长上下文推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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        <div class="mb-2 text-base text-gray-700">
            Semantic Soft Bootstrapping: Long Context Reasoning in LLMs without Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Purbesh Mitra, Sennur Ulukus
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何在不使用强化学习的情况下提升大语言模型的长上下文推理能力。其核心方法是提出一种语义软自举技术，让同一个基础模型通过生成并筛选正确与错误回答作为上下文，自动构建师生训练对，从而学习生成更稳健的逐步推理过程。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的自蒸馏方法属于LLM核心技术进步，其提升长上下文推理能力的设计可直接应用于搜索和推荐系统的复杂任务理解与生成。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05105v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05105v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IT</span><span class="category-tag">cs.LG</span><span class="category-tag">eess.SP</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Long context reasoning in large language models (LLMs) has demonstrated enhancement of their cognitive capabilities via chain-of-thought (CoT) inference. Training such models is usually done via reinforcement learning with verifiable rewards (RLVR) in reasoning based problems, like math and programming. However, RLVR is limited by several bottlenecks, such as, lack of dense reward, and inadequate sample efficiency. As a result, it requires significant compute resources in post-training phase. To overcome these limitations, in this work, we propose \textbf{Semantic Soft Bootstrapping (SSB)}, a self-distillation technique, in which the same base language model plays the role of both teacher and student, but receives different semantic contexts about the correctness of its outcome at training time. The model is first prompted with a math problem and several rollouts are generated. From them, the correct and most common incorrect response are filtered, and then provided to the model in context to produce a more robust, step-by-step explanation with a verified final answer. This pipeline automatically curates a paired teacher-student training set from raw problem-answer data, without any human intervention. This generation process also produces a sequence of logits, which is what the student model tries to match in the training phase just from the bare question alone. In our experiment, Qwen2.5-3B-Instruct on GSM8K dataset via parameter-efficient fine-tuning. We then tested its accuracy on MATH500, and AIME2024 benchmarks. Our experiments show a jump of 10.6%, and 10% improvements in accuracy, respectively, over group relative policy optimization (GRPO), which is a commonly used RLVR algorithm. Our code is available at https://github.com/purbeshmitra/semantic-soft-bootstrapping, and the model, curated dataset is available at https://huggingface.co/purbeshmitra/semantic-soft-bootstrapping.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04748v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>模型耳语：导向向量解锁大型语言模型在测试时的潜力
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Model Whisper: Steering Vectors Unlock Large Language Models' Potential in Test-time
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinyue Kang, Diwei Shi, Li Chen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何高效解锁大语言模型在特定任务或新分布下的推理潜力。核心方法是引入轻量级的测试时引导向量，通过优化该向量最小化模型输出熵，从而引导模型进入更高置信度的内部状态，无需调整模型参数即可激活其相关内在能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的测试时引导向量方法，通过轻量级优化激活LLM内在能力，与推荐/搜索系统中高效适配模型到新任务/分布的核心需求高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:36:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04748v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04748v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    It is a critical challenge to efficiently unlock the powerful reasoning potential of Large Language Models (LLMs) for specific tasks or new distributions. Existing test-time adaptation methods often require tuning model parameters, which is not only computationally expensive but also risks degrading the model's pre-existing abilities.To address this, we introduce a lightweight component, Test-Time Steering Vectors (TTSV), which is prepended to the input while keeping the LLM's parameters entirely frozen. By optimizing the TTSV on test data to minimize the model's output entropy, we steer the model towards an internal state of higher confidence, activating its inherent abilities most relevant to the current task. TTSV is both lightweight and highly efficient to optimize, making it a true plug-and-play enhancement. Extensive experiments validate our approach's effectiveness on both base models and reasoning-enhanced models. For instance, on the MATH500 task, TTSV achieves a 45.88% relative performance gain on the Qwen2.5-Math-7B model and a 16.22% relative gain on the Qwen3-4B model. Furthermore, our approach exhibits robust generalization, with its steering vectors proving highly transferable across diverse tasks.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04746v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>SignRoundV2：在LLMs的极低比特后训练量化中弥合性能差距
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenhua Cheng, Weiwei Zhang, Heng Guo, Haihao Shen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM在极低比特（如2-4比特）后训练量化中的性能下降问题，核心方法是结合梯度信息和量化偏差的快速敏感度度量来指导分层比特分配，并通过轻量级预调优搜索优化量化尺度。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM的极低比特量化技术，直接提升模型部署效率，属于LLM核心技术进步，对搜索推荐广告系统的实际应用有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:35:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04746v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04746v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Extreme low-bit quantization is critical for efficiently deploying Large Language Models (LLMs), yet it often leads to severe performance degradation at 2-bits and even 4-bits (e.g., MXFP4). We present SignRoundV2, a post-training quantization framework that is highly effective even without mixed-precision. SignRoundV2 introduces (1) a fast sensitivity metric that combines gradient information with quantization-induced deviations to guide layer-wise bit allocation, and (2) a lightweight pre-tuning search for quantization scales to improve extremely low-bit quantization. These components allow SignRoundV2 to close the gap with full-precision models. Extensive experiments indicate that our method sustains competitive accuracy for LLMs, achieving production-grade performance with about 1 percent variance at 4-5 bits and strong results even at 2 bits. The implementation is available at https://github.com/intel/auto-round.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04555v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ADAPT：面向预算约束指令微调的任务混合学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ADAPT: Learning Task Mixtures for Budget-Constrained Instruction Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pritam Kadasi, Abhishek Upperwal, Mayank SIngh
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何在有限计算预算下优化多任务指令微调。其核心方法是提出ADAPT元学习算法，通过元梯度动态学习任务采样比例，形成自适应课程，将更多计算资源分配给对下游任务泛化更有益的任务。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种用于多任务指令微调的元学习算法，通过元梯度优化任务采样比例以在有限计算预算下提升模型泛化能力，这直接关联到LLM高效训练技术，对推荐、搜索和广告领域的模型优化具有应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:17:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04555v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04555v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose ADAPT, a meta-learning algorithm that \emph{learns} task sampling proportions under an explicit token budget for multi-task instruction tuning. Instead of fixing task weights by hand, \adapt{} maintains a continuous distribution over tasks and updates it via meta-gradients of a smooth worst-case validation objective, inducing an adaptive curriculum that allocates more tokens to useful tasks while avoiding collapse. We instantiate ADAPT on three $\sim$1B-parameter open-weight LLMs (Gemma-3-1B, LLaMA-3.2-1B, Qwen-0.6B), training on 20 Natural Instructions task types under budgets of $1\%$, $5\%$, and $10\%$ of the available supervised tokens, and compare against strong supervised fine-tuning baselines with uniform and size-proportional mixing. We conduct evaluations on 11 out-of-domain benchmarks spanning reasoning, reading comprehension, code generation, and instruction following, we find that ADAPT matches or slightly improves average downstream performance relative to the best static mixture, while using fewer effective training tokens and reallocating budget toward harder, benchmark-aligned tasks.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04550v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>AdmTree：基于自适应语义树的长上下文压缩
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yangning Li, Shaoshen Chen, Yinghui Li, Yankai Chen, Hai-Tao Zheng, Hui Wang, We...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">主题：解决LLM处理长上下文时因自注意力二次复杂度导致的效率瓶颈问题。核心思想：提出AdmTree框架，通过基于信息密度的动态分段、使用要点令牌构建语义二叉树进行层次化抽象，在最小化可训练参数的同时保持语义保真度。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM处理长上下文的核心瓶颈提出压缩方法，属于Transformer架构效率提升的关键技术，对搜索和推荐系统中的长序列建模有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:04:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04550v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04550v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The quadratic complexity of self-attention constrains Large Language Models (LLMs) in processing long contexts, a capability essential for many advanced applications. Context compression aims to alleviate this computational bottleneck while retaining critical semantic information. However, existing approaches often fall short: explicit methods may compromise local detail, whereas implicit methods can suffer from positional biases, information degradation, or an inability to capture long-range semantic dependencies. We propose AdmTree, a novel framework for adaptive, hierarchical context compression with a central focus on preserving high semantic fidelity while maintaining efficiency. AdmTree dynamically segments input based on information density, utilizing gist tokens to summarize variable-length segments as the leaves of a semantic binary tree. This structure, together with a lightweight aggregation mechanism and a frozen backbone LLM (thereby minimizing new trainable parameters), enables efficient hierarchical abstraction of the context. By preserving fine-grained details alongside global semantic coherence, mitigating positional bias, and dynamically adapting to content, AdmTree robustly retains the semantic information of long contexts.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04350v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ClusterFusion：嵌入引导与LLM适配的混合聚类方法
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiming Xu, Yuan Yuan, Vijay Viswanathan, Graham Neubig
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究传统嵌入方法在领域特定文本聚类中的局限性问题，核心思想是提出一个三阶段混合框架：首先用轻量级嵌入方法引导子集划分，然后用LLM进行主题总结，最后用LLM进行主题分配，将LLM作为聚类的核心引擎而非辅助模块。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出将LLM作为聚类核心的混合框架，直接应用于文本聚类任务，属于LLM在信息组织与检索中的直接应用，与搜索和推荐系统的内容理解与分类高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 00:49:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04350v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04350v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text clustering is a fundamental task in natural language processing, yet traditional clustering algorithms with pre-trained embeddings often struggle in domain-specific contexts without costly fine-tuning. Large language models (LLMs) provide strong contextual reasoning, yet prior work mainly uses them as auxiliary modules to refine embeddings or adjust cluster boundaries. We propose ClusterFusion, a hybrid framework that instead treats the LLM as the clustering core, guided by lightweight embedding methods. The framework proceeds in three stages: embedding-guided subset partition, LLM-driven topic summarization, and LLM-based topic assignment. This design enables direct incorporation of domain knowledge and user preferences, fully leveraging the contextual adaptability of LLMs. Experiments on three public benchmarks and two new domain-specific datasets demonstrate that ClusterFusion not only achieves state-of-the-art performance on standard tasks but also delivers substantial gains in specialized domains. To support future work, we release our newly constructed dataset and results on all benchmarks.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04963v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>GeoPE：面向结构化张量的统一几何位置嵌入
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            GeoPE:A Unified Geometric Positional Embedding for Structured Tensors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yupu Yao, Bowen Yang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究标准视觉Transformer将2D图像展平为1D序列时破坏空间拓扑结构的问题。核心思想是引入几何位置嵌入，通过四元数扩展到3D欧几里得空间，并在李代数中计算几何均值来构建统一的旋转算子，从而创建几何耦合的编码来有效分离空间维度。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种新的Transformer位置编码方法，直接改进Transformer架构的效率与表达能力，属于核心的Transformer技术进步，对推荐、搜索等序列建模任务有潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:31:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04963v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04963v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
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                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Standard Vision Transformers flatten 2D images into 1D sequences, disrupting the natural spatial topology. While Rotary Positional Embedding (RoPE) excels in 1D, it inherits this limitation, often treating spatially distant patches (e.g., at row edges) as sequence neighbors. Existing 2D approaches typically treat spatial axes independently, failing to decouple this false sequential proximity from true spatial distance. To restore the 2D spatial manifold, we introduce Geometric Positional Embedding (GeoPE), a framework that extends rotations to 3D Euclidean space using quaternions. To overcome non-commutativity and ensure symmetry, GeoPE constructs a unified rotational operator by computing the geometric mean in the Lie algebra. This creates a geometrically coupled encoding that effectively separates spatial dimensions. Extensive experiments on image classification, object detection, and 3D semantic segmentation demonstrate that GeoPE consistently outperforms existing 2D RoPE variants and significantly enhances shape bias, confirming its ability to capture true geometric structure.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04738v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>OsmT：通过开源标签感知语言模型桥接OpenStreetMap查询与自然语言
            </a>
        </h3>
        <span class="score-badge bg-blue-100 text-blue-800">
            <i class="fa fa-star mr-1"></i>5/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            OsmT: Bridging OpenStreetMap Queries and Natural Language with Open-source Tag-aware Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhuoyue Wan, Wentao Hu, Chen Jason Zhang, Yuanfeng Song, Shuaimin Li, Ruiqiang X...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何将自然语言查询转换为OpenStreetMap专用的结构化查询语言（OverpassQL）。其核心方法是提出一个开源标签感知语言模型，并引入标签检索增强机制，以利用OSM数据库的层次化标签知识来生成结构正确的查询。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于地理空间数据库查询的特定领域，虽然涉及语言模型与结构化查询的桥接，但其应用范围（OpenStreetMap/OverpassQL）与推荐/搜索/广告的核心业务场景关联较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:24:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04738v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04738v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.DB</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Bridging natural language and structured query languages is a long-standing challenge in the database community. While recent advances in language models have shown promise in this direction, existing solutions often rely on large-scale closed-source models that suffer from high inference costs, limited transparency, and lack of adaptability for lightweight deployment. In this paper, we present OsmT, an open-source tag-aware language model specifically designed to bridge natural language and Overpass Query Language (OverpassQL), a structured query language for accessing large-scale OpenStreetMap (OSM) data. To enhance the accuracy and structural validity of generated queries, we introduce a Tag Retrieval Augmentation (TRA) mechanism that incorporates contextually relevant tag knowledge into the generation process. This mechanism is designed to capture the hierarchical and relational dependencies present in the OSM database, addressing the topological complexity inherent in geospatial query formulation. In addition, we define a reverse task, OverpassQL-to-Text, which translates structured queries into natural language explanations to support query interpretation and improve user accessibility. We evaluate OsmT on a public benchmark against strong baselines and observe consistent improvements in both query generation and interpretation. Despite using significantly fewer parameters, our model achieves competitive accuracy, demonstrating the effectiveness of open-source pre-trained language models in bridging natural language and structured query languages within schema-rich geospatial environments.
                </div>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04588v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>UserSimCRS v2：基于模拟的对话式推荐系统评估
            </a>
        </h3>
        <span class="score-badge bg-blue-100 text-blue-800">
            <i class="fa fa-star mr-1"></i>4/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            UserSimCRS v2: Simulation-Based Evaluation for Conversational Recommender Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nolwenn Bernard, Krisztian Balog
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究对话推荐系统缺乏仿真评估资源的问题，核心方法是升级UserSimCRS工具包，通过增强议程模拟器、引入LLM模拟器、扩展系统集成和LLM评估功能来改进评估框架。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注对话推荐系统的仿真评估工具包升级，虽然涉及LLM作为模拟器和评估器，但其核心是工具开发而非算法创新，与用户关注的LLM在推荐/搜索/广告领域的直接应用或架构创新关联较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 09:07:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04588v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04588v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Resources for simulation-based evaluation of conversational recommender systems (CRSs) are scarce. The UserSimCRS toolkit was introduced to address this gap. In this work, we present UserSimCRS v2, a significant upgrade aligning the toolkit with state-of-the-art research. Key extensions include an enhanced agenda-based user simulator, introduction of large language model-based simulators, integration for a wider range of CRSs and datasets, and new LLM-as-a-judge evaluation utilities. We demonstrate these extensions in a case study.
                </div>
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04625v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>重新思考解耦知识蒸馏：从预测分布视角出发
            </a>
        </h3>
        <span class="score-badge bg-blue-100 text-blue-800">
            <i class="fa fa-star mr-1"></i>4/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Rethinking Decoupled Knowledge Distillation: A Predictive Distribution Perspective
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bowen Zheng, Ran Cheng
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究知识蒸馏中解耦策略的优化问题，核心思想是从预测分布视角重新分析解耦知识蒸馏，提出通过划分顶部logit来增强非顶部logit间的关系，并设计更通用的解耦损失函数来改进知识传递效果。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦知识蒸馏的算法改进，属于通用模型压缩技术，虽可应用于推荐系统等领域的模型部署优化，但并非直接针对搜索、推荐或广告的核心问题或LLM应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 09:56:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04625v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04625v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In the history of knowledge distillation, the focus has once shifted over time from logit-based to feature-based approaches. However, this transition has been revisited with the advent of Decoupled Knowledge Distillation (DKD), which re-emphasizes the importance of logit knowledge through advanced decoupling and weighting strategies. While DKD marks a significant advancement, its underlying mechanisms merit deeper exploration. As a response, we rethink DKD from a predictive distribution perspective. First, we introduce an enhanced version, the Generalized Decoupled Knowledge Distillation (GDKD) loss, which offers a more versatile method for decoupling logits. Then we pay particular attention to the teacher model's predictive distribution and its impact on the gradients of GDKD loss, uncovering two critical insights often overlooked: (1) the partitioning by the top logit considerably improves the interrelationship of non-top logits, and (2) amplifying the focus on the distillation loss of non-top logits enhances the knowledge extraction among them. Utilizing these insights, we further propose a streamlined GDKD algorithm with an efficient partition strategy to handle the multimodality of teacher models' predictive distribution. Our comprehensive experiments conducted on a variety of benchmarks, including CIFAR-100, ImageNet, Tiny-ImageNet, CUB-200-2011, and Cityscapes, demonstrate GDKD's superior performance over both the original DKD and other leading knowledge distillation methods. The code is available at https://github.com/ZaberKo/GDKD.
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            <a href="https://www.alphaxiv.org/abs/2512.04834v1" target="_blank" rel="noopener noreferrer">
                大语言模型是否真正具备多语言能力？探索大语言模型在信息检索中的零样本多语言能力：以意大利医疗用例为例
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Are LLMs Truly Multilingual? Exploring Zero-Shot Multilingual Capability of LLMs for Information Retrieval: An Italian Healthcare Use Case
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vignesh Kumar Kembu, Pierandrea Morandini, Marta Bianca Maria Ranzini, Antonino ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究LLMs在医疗领域信息检索中的多语言能力，这属于特定领域应用而非核心推荐/搜索/广告系统进展。虽然涉及信息检索技术，但其医疗用例焦点使其与当前关注的核心领域和直接应用不符。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:17:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04834v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04834v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have become a key topic in AI and NLP, transforming sectors like healthcare, finance, education, and marketing by improving customer service, automating tasks, providing insights, improving diagnostics, and personalizing learning experiences. Information extraction from clinical records is a crucial task in digital healthcare. Although traditional NLP techniques have been used for this in the past, they often fall short due to the complexity, variability of clinical language, and high inner semantics in the free clinical text. Recently, Large Language Models (LLMs) have become a powerful tool for better understanding and generating human-like text, making them highly effective in this area. In this paper, we explore the ability of open-source multilingual LLMs to understand EHRs (Electronic Health Records) in Italian and help extract information from them in real-time. Our detailed experimental campaign on comorbidity extraction from EHR reveals that some LLMs struggle in zero-shot, on-premises settings, and others show significant variation in performance, struggling to generalize across various diseases when compared to native pattern matching and manual annotations.
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            <a href="https://www.alphaxiv.org/abs/2512.05012v1" target="_blank" rel="noopener noreferrer">
                事实性与透明度即RAG所需全部！自解释对比证据重排序
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Factuality and Transparency Are All RAG Needs! Self-Explaining Contrastive Evidence Re-ranking
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Francielle Vargas, Daniel Pedronette
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注检索增强生成（RAG）的事实性和透明度问题，属于LLM评估和可靠性领域。虽然RAG技术可能间接应用于搜索系统，但论文标题明确聚焦于事实性验证和解释性，这更接近NLP评估基准和幻觉缓解等无关主题，而非推荐/搜索/广告领域的核心进展或直接应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:24:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05012v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05012v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by fine-tuning embeddings with contrastive learning and generating token-level attribution rationales for each retrieved passage. Hard negatives are automatically selected using a subjectivity-based criterion, forcing the model to pull factual rationales closer while pushing subjective or misleading explanations apart. As a result, the method creates an embedding space explicitly aligned with evidential reasoning. We evaluated our method on clinical trial reports, and initial experimental results show that CER improves retrieval accuracy, mitigates the potential for hallucinations in RAG systems, and provides transparent, evidence-based retrieval that enhances reliability, especially in safety-critical domains.
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            <a href="https://www.alphaxiv.org/abs/2512.04868v1" target="_blank" rel="noopener noreferrer">
                SEAL：面向知识图谱对话式问答的自进化智能体学习
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Wang, Jialun Zhong, Changcheng Wang, Zhujun Nie, Zheng Li, Shunyu Yao, Yanze...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注对话式问答和知识图谱，属于特定应用场景的NLP任务。虽然涉及智能体学习，但未明确展示在推荐系统、搜索或广告领域的直接应用潜力。核心内容更偏向对话系统和知识推理，而非推荐/搜索/广告的核心技术或LLM在这些领域的应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:52:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04868v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04868v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches, whether end-to-end semantic parsing or stepwise agent-based reasoning, often suffer from structural inaccuracies and prohibitive computational costs, particularly when processing intricate queries over large knowledge graphs. To address these limitations, we introduce SEAL, a novel two-stage semantic parsing framework grounded in self-evolving agentic learning. In the first stage, a large language model (LLM) extracts a minimal S-expression core that captures the essential semantics of the input query. This core is then refined by an agentic calibration module, which corrects syntactic inconsistencies and aligns entities and relations precisely with the underlying knowledge graph. The second stage employs template-based completion, guided by question-type prediction and placeholder instantiation, to construct a fully executable S-expression. This decomposition not only simplifies logical form generation but also significantly enhances structural fidelity and linking efficiency. Crucially, SEAL incorporates a self-evolving mechanism that integrates local and global memory with a reflection module, enabling continuous adaptation from dialog history and execution feedback without explicit retraining. Extensive experiments on the SPICE benchmark demonstrate that SEAL achieves state-of-the-art performance, especially in multi-hop reasoning, comparison, and aggregation tasks. The results validate notable gains in both structural accuracy and computational efficiency, underscoring the framework's capacity for robust and scalable conversational reasoning.
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            <a href="https://www.alphaxiv.org/abs/2512.04601v1" target="_blank" rel="noopener noreferrer">
                自然语言演员-评论家：语言空间中的可扩展离策略学习
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Natural Language Actor-Critic: Scalable Off-Policy Learning in Language Space
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joey Hong, Kang Liu, Zhan Ling, Jiecao Chen, Sergey Levine
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及强化学习（RL）与语言模型的结合，属于RL范畴。虽然标题提到语言空间，但核心是强化学习算法（演员-评论家），而非直接应用于推荐/搜索/广告的LLM技术。根据排除标准，RL论文若无明确相关性则不予考虑，因此评分较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 09:21:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04601v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04601v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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                    Large language model (LLM) agents -- LLMs that dynamically interact with an environment over long horizons -- have become an increasingly important area of research, enabling automation in complex tasks involving tool-use, web browsing, and dialogue with people. In the absence of expert demonstrations, training LLM agents has relied on policy gradient methods that optimize LLM policies with respect to an (often sparse) reward function. However, in long-horizon tasks with sparse rewards, learning from trajectory-level rewards can be noisy, leading to training that is unstable and has high sample complexity. Furthermore, policy improvement hinges on discovering better actions through exploration, which can be difficult when actions lie in natural language space. In this paper, we propose Natural Language Actor-Critic (NLAC), a novel actor-critic algorithm that trains LLM policies using a generative LLM critic that produces natural language rather than scalar values. This approach leverages the inherent strengths of LLMs to provide a richer and more actionable training signal; particularly, in tasks with large, open-ended action spaces, natural language explanations for why an action is suboptimal can be immensely useful for LLM policies to reason how to improve their actions, without relying on random exploration. Furthermore, our approach can be trained off-policy without policy gradients, offering a more data-efficient and stable alternative to existing on-policy methods. We present results on a mixture of reasoning, web browsing, and tool-use with dialogue tasks, demonstrating that NLAC shows promise in outperforming existing training approaches and offers a more scalable and stable training paradigm for LLM agents.
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            <a href="https://www.alphaxiv.org/abs/2512.04457v1" target="_blank" rel="noopener noreferrer">
                RapidUn：基于影响力驱动的参数重加权用于高效大语言模型遗忘
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            <i class="fa fa-star mr-1"></i>3/10
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            RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guoshenghui Zhao, Huawei Lin, Weijie Zhao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注大语言模型的遗忘技术，属于LLM核心技术的效率优化方向，可能应用于RecSys/Search/Ads领域的数据合规更新或模型修正。但论文标题未明确展示其在这些领域的直接应用潜力，且可能偏向隐私合规等非技术焦点，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:00:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04457v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04457v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Removing specific data influence from large language models (LLMs) remains challenging, as retraining is costly and existing approximate unlearning methods are often unstable. The challenge is exacerbated when the forget set is small or imbalanced. We introduce RapidUn, an influence-driven and parameter-efficient unlearning framework. It first estimates per-sample influence through a fast estimation module, then maps these scores into adaptive update weights that guide selective parameter updates -- forgetting harmful behavior while retaining general knowledge. On Mistral-7B and Llama-3-8B across Dolly-15k and Alpaca-57k, RapidUn achieves up to 100 times higher efficiency than full retraining and consistently outperforms Fisher, GA, and LoReUn on both in-distribution and out-of-distribution forgetting. These results establish influence-guided parameter reweighting as a scalable and interpretable paradigm for LLM unlearning.
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            <a href="https://www.alphaxiv.org/abs/2512.05116v1" target="_blank" rel="noopener noreferrer">
                用于流匹配对齐的价值梯度引导
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Value Gradient Guidance for Flow Matching Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhen Liu, Tim Z. Xiao, Carles Domingo-Enrich, Weiyang Liu, Dinghuai Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及强化学习中的价值函数和流匹配对齐技术，这些可能间接支持推荐或搜索系统的策略优化。然而，论文标题未明确说明与推荐系统、搜索或广告的直接应用，且强化学习论文若无明确相关性则属于无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05116v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05116v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    While methods exist for aligning flow matching models--a popular and effective class of generative models--with human preferences, existing approaches fail to achieve both adaptation efficiency and probabilistically sound prior preservation. In this work, we leverage the theory of optimal control and propose VGG-Flow, a gradient-matching-based method for finetuning pretrained flow matching models. The key idea behind this algorithm is that the optimal difference between the finetuned velocity field and the pretrained one should be matched with the gradient field of a value function. This method not only incorporates first-order information from the reward model but also benefits from heuristic initialization of the value function to enable fast adaptation. Empirically, we show on a popular text-to-image flow matching model, Stable Diffusion 3, that our method can finetune flow matching models under limited computational budgets while achieving effective and prior-preserving alignment.
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            <a href="https://www.alphaxiv.org/abs/2512.04686v1" target="_blank" rel="noopener noreferrer">
                迈向视觉-语言模型中的跨视角点对应关系
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Towards Cross-View Point Correspondence in Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yipu Wang, Yuheng Ji, Yuyang Liu, Enshen Zhou, Ziqiang Yang, Yuxuan Tian, Ziheng...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注视觉-语言模型中的跨视角对应问题，属于VLM技术范畴。虽然VLM技术可能启发异构数据处理（如将用户序列和上下文特征视为不同模态），但论文标题未明确指向推荐/搜索/广告领域的应用，更多是计算机视觉与NLP的交叉研究。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:30:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04686v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04686v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Cross-view correspondence is a fundamental capability for spatial understanding and embodied AI. However, it is still far from being realized in Vision-Language Models (VLMs), especially in achieving precise point-level correspondence, which is crucial for precise affordance interaction. So we propose the Cross-View Point Correspondence (CVPC) task and CrossPoint-Bench, a comprehensive benchmark with hierarchical design, inspired by the human cognitive process of "perceive", "reason", and "correspond". Our evaluation shows the state-of-the-art models (e.g., Gemini-2.5-Pro) still fall far behind humans, with a gap of over 54.65% in overall accuracy, exposing a challenge in transitioning from coarse-grained judgement to fine-grained coordinate prediction. To address this problem, we construct CrossPoint-378K, a dataset with 378K question-answering pairs across 900 scenes, focused on actionable affordance regions that better reflect real-world manipulation and interaction scenarios. Furthermore, we propose CroPond that trained on the CrossPoint-378K dataset. Our CroPond achieves state-of-the-art performance on CrossPoint-Bench, surpassing Gemini-2.5-Pro by 39.7% accuracy, which offers a foundation for advancing future work on cross-view correspondence. The benchmark, dataset, and model are publicly available at https://github.com/WangYipu2002/CrossPoint.
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            <a href="https://www.alphaxiv.org/abs/2512.04790v1" target="_blank" rel="noopener noreferrer">
                面向步行性与城市探索的空间增强检索增强生成
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            Spatially-Enhanced Retrieval-Augmented Generation for Walkability and Urban Discovery
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Maddalena Amendola, Chiara Pugliese, Raffaele Perego, Chiara Renso
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及检索增强生成（RAG）技术，属于LLM应用范畴，但其应用场景聚焦于城市步行性和空间发现，与推荐系统、搜索或广告的核心领域关联较弱。虽然RAG技术本身具有在搜索和推荐中应用的潜力，但论文的具体应用方向（城市空间）偏离了您关注的商业应用领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 13:37:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04790v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04790v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have become foundational tools in artificial intelligence, supporting a wide range of applications beyond traditional natural language processing, including urban systems and tourist recommendations. However, their tendency to hallucinate and their limitations in spatial retrieval and reasoning are well known, pointing to the need for novel solutions. Retrieval-augmented generation (RAG) has recently emerged as a promising way to enhance LLMs with accurate, domain-specific, and timely information. Spatial RAG extends this approach to tasks involving geographic understanding. In this work, we introduce WalkRAG, a spatial RAG-based framework with a conversational interface for recommending walkable urban itineraries. Users can request routes that meet specific spatial constraints and preferences while interactively retrieving information about the path and points of interest (POIs) along the way. Preliminary results show the effectiveness of combining information retrieval, spatial reasoning, and LLMs to support urban discovery.
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            <a href="https://www.alphaxiv.org/abs/2512.05100v1" target="_blank" rel="noopener noreferrer">
                通过格式强化学习实现结构化文档翻译
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Structured Document Translation via Format Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haiyue Song, Johannes Eschbach-Dymanus, Hour Kaing, Sumire Honda, Hideki Tanaka,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文档翻译中的格式保持问题，属于机器翻译领域。虽然强化学习技术可能在其他领域有应用，但论文标题明确指向文档翻译任务，与推荐系统、搜索或广告的核心技术进展没有直接关联。强化学习在推荐系统中的应用通常涉及序列决策或在线优化，而非文档格式处理。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:58:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05100v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05100v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent works on structured text translation remain limited to the sentence level, as they struggle to effectively handle the complex document-level XML or HTML structures. To address this, we propose \textbf{Format Reinforcement Learning (FormatRL)}, which employs Group Relative Policy Optimization on top of a supervised fine-tuning model to directly optimize novel structure-aware rewards: 1) TreeSim, which measures structural similarity between predicted and reference XML trees and 2) Node-chrF, which measures translation quality at the level of XML nodes. Additionally, we apply StrucAUC, a fine-grained metric distinguishing between minor errors and major structural failures. Experiments on the SAP software-documentation benchmark demonstrate improvements across six metrics and an analysis further shows how different reward functions contribute to improvements in both structural and translation quality.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04987v1" target="_blank" rel="noopener noreferrer">
                Nex-N1：通过统一生态系统训练用于大规模环境构建的智能体模型
            </a>
        </h3>
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            Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nex-AGI Team, :, Yuxuan Cai, Lu Chen, Qiaoling Chen, Yuyang Ding, Liwen Fan, Wen...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题主要关注智能体模型和大规模环境构建的统一生态系统，这属于强化学习或智能体训练领域。虽然大规模环境构建可能间接支持推荐系统或搜索的模拟环境，但标题没有明确提及推荐系统、搜索、广告、LLM或Transformer技术，也没有展示与异构数据统一建模的直接联系。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:57:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04987v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04987v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incentive-driven decision making. However, this transition is significantly impeded by the lack of scalable infrastructure capable of constructing high-quality interaction signals for effective policy learning. To address this, we introduce a comprehensive method designed to systematically scale the diversity and complexity of interactive environments. Our method realizes this scaling by addressing three orthogonal dimensions: (1) Complexity: NexAU, a flexible agent framework that supports building complex agent hierarchies via simple configurations; (2) Diversity: NexA4A automatically generates diverse agent hierarchies from natural language to cover infinite domains; and (3) Fidelity: NexGAP bridges the simulation-reality gap by integrating dynamic real-world environment for grounded trajectories synthesis. We train Nex-N1 upon the diverse and complex interactive environments established by our infrastructure. Empirical results on benchmarks such as SWE-bench and tau2 demonstrate that Nex-N1 consistently outperforms SOTA open-source models and achieves competitive performance against frontier proprietary models on complex agentic tasks. We open-source the Nex ecosystem and model weights to facilitate further research.
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            <a href="https://www.alphaxiv.org/abs/2512.04957v1" target="_blank" rel="noopener noreferrer">
                大语言模型所知不止于词汇：基于句法、隐喻与语音学的体裁研究
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            LLMs Know More Than Words: A Genre Study with Syntax, Metaphor & Phonetics
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weiye Shi, Zhaowei Zhang, Shaoheng Yan, Yaodong Yang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究LLMs在句法、隐喻和语音学等语言学层面的能力，属于对LLM内部工作机制的探索。虽然涉及LLM技术，但其研究焦点是纯粹的语言学分析（句法、隐喻、语音学），与推荐系统、搜索或广告中的实际应用场景（如查询理解、内容表征、用户意图建模）缺乏直接关联。论文未提出任何在RecSys/Search/Ads领域的具体应用方向或技术启示。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:26:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04957v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04957v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) demonstrate remarkable potential across diverse language related tasks, yet whether they capture deeper linguistic properties, such as syntactic structure, phonetic cues, and metrical patterns from raw text remains unclear. To analysis whether LLMs can learn these features effectively and apply them to important nature language related tasks, we introduce a novel multilingual genre classification dataset derived from Project Gutenberg, a large-scale digital library offering free access to thousands of public domain literary works, comprising thousands of sentences per binary task (poetry vs. novel;drama vs. poetry;drama vs. novel) in six languages (English, French, German, Italian, Spanish, and Portuguese). We augment each with three explicit linguistic feature sets (syntactic tree structures, metaphor counts, and phonetic metrics) to evaluate their impact on classification performance. Experiments demonstrate that although LLM classifiers can learn latent linguistic structures either from raw text or from explicitly provided features, different features contribute unevenly across tasks, which underscores the importance of incorporating more complex linguistic signals during model training.
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            <a href="https://www.alphaxiv.org/abs/2512.04844v1" target="_blank" rel="noopener noreferrer">
                通过源屏蔽更新缓解大型语言模型目标语言适应中的灾难性遗忘
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        <div class="mb-2 text-base text-gray-700">
            Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio, Nikolaos Aletras
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在语言适应中的灾难性遗忘问题，这属于LLM微调技术范畴。虽然LLM适应技术可能间接应用于多语言搜索或推荐系统，但论文标题没有明确指向RecSys/Search/Ads领域的应用，也没有涉及Transformer架构创新或异构数据建模等核心关注点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:28:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04844v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04844v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the reliance on costly specialized target language labeled data and catastrophic forgetting during adaptation. We tackle this challenge under a realistic, low-resource constraint: adapting instruct LLMs using only unlabeled target language data. We introduce Source-Shielded Updates (SSU), a selective parameter update strategy that proactively preserves source knowledge. Using a small set of source data and a parameter importance scoring method, SSU identifies parameters critical to maintaining source abilities. It then applies a column-wise freezing strategy to protect these parameters before adaptation. Experiments across five typologically diverse languages and 7B and 13B models demonstrate that SSU successfully mitigates catastrophic forgetting. It reduces performance degradation on monolingual source tasks to just 3.4% (7B) and 2.8% (13B) on average, a stark contrast to the 20.3% and 22.3% from full fine-tuning. SSU also achieves target-language performance highly competitive with full fine-tuning, outperforming it on all benchmarks for 7B models and the majority for 13B models.
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            <a href="https://www.alphaxiv.org/abs/2512.04838v1" target="_blank" rel="noopener noreferrer">
                DAMASHA：通过人类可解释归因的分割检测混合对抗文本中的AI内容
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            DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>L. D. M. S. Sai Teja, N. Siva Gopala Krishna, Ufaq Khan, Muhammad Haris Khan, Pa...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注AI生成文本的检测和对抗性文本分析，这属于AI安全/内容鉴别的技术范畴。虽然广告领域可能涉及虚假内容检测，但论文标题明确指向“对抗文本”和“人类可解释归因”，这更偏向AI安全、内容鉴别或NLP评估方向，与您关注的推荐系统/搜索/广告的核心算法、LLM技术应用、Transformer架构改进或异构数据统一建模等焦点领域直接相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:21:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04838v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04838v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    In the age of advanced large language models (LLMs), the boundaries between human and AI-generated text are becoming increasingly blurred. We address the challenge of segmenting mixed-authorship text, that is identifying transition points in text where authorship shifts from human to AI or vice-versa, a problem with critical implications for authenticity, trust, and human oversight. We introduce a novel framework, called Info-Mask for mixed authorship detection that integrates stylometric cues, perplexity-driven signals, and structured boundary modeling to accurately segment collaborative human-AI content. To evaluate the robustness of our system against adversarial perturbations, we construct and release an adversarial benchmark dataset Mixed-text Adversarial setting for Segmentation (MAS), designed to probe the limits of existing detectors. Beyond segmentation accuracy, we introduce Human-Interpretable Attribution (HIA overlays that highlight how stylometric features inform boundary predictions, and we conduct a small-scale human study assessing their usefulness. Across multiple architectures, Info-Mask significantly improves span-level robustness under adversarial conditions, establishing new baselines while revealing remaining challenges. Our findings highlight both the promise and limitations of adversarially robust, interpretable mixed-authorship detection, with implications for trust and oversight in human-AI co-authorship.
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            <a href="https://www.alphaxiv.org/abs/2512.04763v1" target="_blank" rel="noopener noreferrer">
                MemLoRA：用于设备内存系统的专家适配器蒸馏
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        <div class="mb-2 text-base text-gray-700">
            MemLoRA: Distilling Expert Adapters for On-Device Memory Systems
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Massimo Bini, Ondrej Bohdal, Umberto Michieli, Zeynep Akata, Mete Ozay, Taha Cer...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及LoRA（低秩适配）和专家蒸馏技术，这些属于Transformer效率改进的范畴，可能应用于模型压缩或高效微调。然而，标题明确指向“设备内存系统”，这更偏向硬件/系统优化而非直接应用于推荐/搜索/广告的核心算法或架构创新，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:56:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04763v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04763v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Memory-augmented Large Language Models (LLMs) have demonstrated remarkable consistency during prolonged dialogues by storing relevant memories and incorporating them as context. Such memory-based personalization is also key in on-device settings that allow users to keep their conversations and data private. However, memory-augmented systems typically rely on LLMs that are too costly for local on-device deployment. Even though Small Language Models (SLMs) are more suitable for on-device inference than LLMs, they cannot achieve sufficient performance. Additionally, these LLM-based systems lack native visual capabilities, limiting their applicability in multimodal contexts. In this paper, we introduce (i) MemLoRA, a novel memory system that enables local deployment by equipping SLMs with specialized memory adapters, and (ii) its vision extension MemLoRA-V, which integrates small Vision-Language Models (SVLMs) to memory systems, enabling native visual understanding. Following knowledge distillation principles, each adapter is trained separately for specific memory operations$\unicode{x2013}$knowledge extraction, memory update, and memory-augmented generation. Equipped with memory adapters, small models enable accurate on-device memory operations without cloud dependency. On text-only operations, MemLoRA outperforms 10$\times$ larger baseline models (e.g., Gemma2-27B) and achieves performance comparable to 60$\times$ larger models (e.g., GPT-OSS-120B) on the LoCoMo benchmark. To evaluate visual understanding operations instead, we extend LoCoMo with challenging Visual Question Answering tasks that require direct visual reasoning. On this, our VLM-integrated MemLoRA-V shows massive improvements over caption-based approaches (81.3 vs. 23.7 accuracy) while keeping strong performance in text-based tasks, demonstrating the efficacy of our method in multimodal contexts.
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            <a href="https://www.alphaxiv.org/abs/2512.04753v1" target="_blank" rel="noopener noreferrer">
                EtCon：先编辑后整合以实现可靠的知识编辑
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            <i class="fa fa-star mr-1"></i>2/10
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            EtCon: Edit-then-Consolidate for Reliable Knowledge Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruilin Li, Yibin Wang, Wenhong Zhu, Chenglin Li, Jinghao Zhang, Chenliang Li, Ju...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注知识编辑技术，属于LLM模型维护和更新范畴，与您关注的推荐/搜索/广告领域核心进展、LLM技术趋势或Transformer架构创新无直接关联。虽然知识编辑可能间接影响推荐系统中使用的LLM模型，但论文本身并未明确涉及推荐/搜索/广告的具体应用场景或技术需求。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:43:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04753v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04753v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge editing aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, proving effective for making selective edits. However, a significant gap exists between their performance in controlled, teacher-forcing evaluations and their real-world effectiveness in lifelong learning scenarios, which greatly limits their practical applicability. This work's empirical analysis reveals two recurring issues associated with this gap: (1) Most traditional methods lead the edited model to overfit to the new fact, thereby degrading pre-trained capabilities; (2) There is a critical absence of a knowledge consolidation stage, leaving new facts insufficiently integrated into LLMs' inference-time behavior under autoregressive generation, thereby leading to a mismatch between parametric knowledge and actual generation behavior. To this end, we propose Edit-then-Consolidate, a novel knowledge editing paradigm that aims to bridge the gap between theoretical knowledge editing methods and their real-world applicability. Specifically, (1) our framework mitigates overfitting via Targeted Proximal Supervised Fine-Tuning (TPSFT) that localizes the edit via a trust-region objective to limit policy drift; (2) Then, a consolidation stage using Group Relative Policy Optimization (GRPO) aligns the edited knowledge with CoT-based inference policy by optimizing trajectory-level behavior under comprehensive reward signals. Extensive experiments demonstrate our framework consistently improves editing reliability and generalization under real-world evaluations, while better preserving locality and pre-trained capabilities.
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            <a href="https://www.alphaxiv.org/abs/2512.04643v1" target="_blank" rel="noopener noreferrer">
                SEASON：通过自诊断对比解码缓解视频大语言模型中的时序幻觉
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            SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chang-Hsun Wu, Kai-Po Chang, Yu-Yang Sheng, Hung-Kai Chung, Kuei-Chun Wang, Yu-C...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要针对视频大语言模型中的时序幻觉问题，属于视觉-语言交叉领域，但未明确涉及推荐系统、搜索或广告应用。虽然提到了对比解码技术，但其应用场景局限于视频理解，与当前关注的异构数据统一建模或LLM在推荐/搜索中的直接应用关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 10:17:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04643v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04643v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video Large Language Models (VideoLLMs) have shown remarkable progress in video understanding. However, these models still struggle to effectively perceive and exploit rich temporal information in videos when responding to user queries. Therefore, they often generate descriptions of events that are temporal inconsistent or causally implausible, causing severe hallucination issues. While most prior studies have focused on spatial hallucinations (e.g. object mismatches), temporal reasoning in video understanding remains relatively underexplored. To address this issue, we propose Self-Diagnostic Contrastive Decoding (SEASON), a training-free method that adaptively enhances temporal and spatial faithfulness for each output token. It achieves this by dynamically diagnosing each token's hallucination tendency and applying adaptive contrastive decoding against its corresponding temporal and spatial negatives. Extensive experiments demonstrate that SEASON outperforms all existing training-free hallucination mitigation approaches on three hallucination examination benchmarks, while further improves VideoLLMs across four general video understanding benchmarks. The code will be released upon acceptance.
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            <a href="https://www.alphaxiv.org/abs/2512.04545v1" target="_blank" rel="noopener noreferrer">
                EvoEdit：通过潜在扰动增强与知识驱动参数融合实现终身自由文本知识编辑
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            EvoEdit: Lifelong Free-Text Knowledge Editing through Latent Perturbation Augmentation and Knowledge-driven Parameter Fusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pengfei Cao, Zeao Ji, Daojian Zeng, Jun Zhao, Kang Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的知识编辑技术，属于纯粹的LLM中心化主题，与您关注的推荐系统/搜索/广告核心领域进展、直接应用或使能技术无关。虽然知识编辑技术可能间接影响模型在特定领域的表现，但论文标题未表明任何针对RecSys/Search/Ads的具体应用或相关性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:55:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04545v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04545v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Adjusting the outdated knowledge of large language models (LLMs) after deployment remains a major challenge. This difficulty has spurred the development of knowledge editing, which seeks to accurately and efficiently modify a model's internal (parametric) knowledge without retraining it from scratch. However, existing methods suffer from two limitations. First, they depend on structured triplets that are misaligned with the free-text nature of LLM pretraining and fail to capture the nuanced relationships among facts. Second, they typically support one-time knowledge updates, with relatively limited research on the problem of sequential or lifelong editing. To address these gaps, we propose a new task, Lifelong Free-text Knowledge Editing (LF-Edit), which enables models to incorporate updates expressed in natural language and supports continual editing over time. Despite its promise, LF-Edit faces the dual challenge of integrating new knowledge while mitigating the forgetting of prior information. To foster research on this new task, we construct a large-scale benchmark, Multi-Rank Lifelong Free-text Editing Benchmark (MRLF-Bench), containing 16,835 free-text edit requests. We further design a cognitively inspired multi-rank evaluation framework encompassing four levels: memorization, understanding, constrained comprehension, and reasoning. To tackle the challenges inherent in LF-Edit, we introduce a novel approach named EvoEdit that enhances knowledge injection through Latent Perturbation Augmentation and preserves prior information via Knowledge-driven Parameter Fusion. Experimental results demonstrate that EvoEdit substantially outperforms existing knowledge editing methods on the proposed LF-Edit task.
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            <a href="https://www.alphaxiv.org/abs/2512.04492v1" target="_blank" rel="noopener noreferrer">
                MSME：一种用于零样本立场检测的多阶段多专家框架
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            MSME: A Multi-Stage Multi-Expert Framework for Zero-Shot Stance Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuanshuo Zhang, Aohua Li, Bo Chen, Jingbo Sun, Xiaobing Zhao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及零样本立场检测，这属于自然语言处理中的特定任务，与推荐系统、搜索或广告的核心领域进展或直接应用没有明确关联。虽然多阶段多专家框架可能在架构设计上有一定启发，但论文标题未表明其在推荐/搜索/广告场景中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:56:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04492v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04492v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    LLM-based approaches have recently achieved impressive results in zero-shot stance detection. However, they still struggle in complex real-world scenarios, where stance understanding requires dynamic background knowledge, target definitions involve compound entities or events that must be explicitly linked to stance labels, and rhetorical devices such as irony often obscure the author's actual intent. To address these challenges, we propose MSME, a Multi-Stage, Multi-Expert framework for zero-shot stance detection. MSME consists of three stages: (1) Knowledge Preparation, where relevant background knowledge is retrieved and stance labels are clarified; (2) Expert Reasoning, involving three specialized modules-Knowledge Expert distills salient facts and reasons from a knowledge perspective, Label Expert refines stance labels and reasons accordingly, and Pragmatic Expert detects rhetorical cues such as irony to infer intent from a pragmatic angle; (3) Decision Aggregation, where a Meta-Judge integrates all expert analyses to produce the final stance prediction. Experiments on three public datasets show that MSME achieves state-of-the-art performance across the board.
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            <a href="https://www.alphaxiv.org/abs/2512.04386v1" target="_blank" rel="noopener noreferrer">
                MASE：通过模型无关显著性估计实现可解释的NLP模型
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            MASE: Interpretable NLP Models via Model-Agnostic Saliency Estimation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhou Yang, Shunyan Luo, Jiazhen Zhu, Fang Jin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于NLP模型的可解释性方法，属于模型解释性技术领域。虽然可解释性在推荐/搜索系统中可能有辅助价值，但论文标题明确限定在NLP领域，没有显示与推荐系统、搜索或广告的直接联系，也不属于核心LLM进展、Transformer架构创新或异构数据建模等当前关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 02:20:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04386v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04386v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Deep neural networks (DNNs) have made significant strides in Natural Language Processing (NLP), yet their interpretability remains elusive, particularly when evaluating their intricate decision-making processes. Traditional methods often rely on post-hoc interpretations, such as saliency maps or feature visualization, which might not be directly applicable to the discrete nature of word data in NLP. Addressing this, we introduce the Model-agnostic Saliency Estimation (MASE) framework. MASE offers local explanations for text-based predictive models without necessitating in-depth knowledge of a model's internal architecture. By leveraging Normalized Linear Gaussian Perturbations (NLGP) on the embedding layer instead of raw word inputs, MASE efficiently estimates input saliency. Our results indicate MASE's superiority over other model-agnostic interpretation methods, especially in terms of Delta Accuracy, positioning it as a promising tool for elucidating the operations of text-based models in NLP.
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                通过自增强对比对齐缓解多模态大语言模型中的物体与动作幻觉
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            Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kai-Po Chang, Wei-Yuan Cheng, Chi-Pin Huang, Fu-En Yang, Yu-Chiang Frank Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态LLM中的幻觉缓解问题，这属于纯粹的NLP中心化主题，与您关注的RecSys/Search/Ads核心领域进展、使能技术或直接应用无关。虽然涉及多模态模型，但焦点是幻觉而非异构数据统一建模，且没有明确指向推荐、搜索或广告领域的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 01:05:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04356v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04356v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Recent advancement in multimodal LLMs (MLLMs) has demonstrated their remarkable capability to generate descriptive captions for input videos. However, these models suffer from factual inaccuracies in the generated descriptions, causing severe hallucination issues. While prior works have explored alleviating hallucinations for static images, jointly mitigating visual object and temporal action hallucinations for dynamic videos remains a challenging and unsolved task. To tackle this challenge, we propose a Self-Augmented Contrastive Alignment (SANTA) framework for enabling object and action faithfulness by exempting the spurious correlations and enforcing the emphasis on visual facts. SANTA employs a hallucinative self-augmentation scheme to identify the potential hallucinations that lie in the MLLM and transform the original captions to the contrasted negatives. Furthermore, we develop a tracklet-phrase contrastive alignment to match the regional objects and relation-guided actions with their corresponding visual and temporal phrases. Extensive experiments demonstrate that SANTA outperforms existing methods in alleviating object and action hallucinations, yielding superior performance on the hallucination examination benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2512.05111v1" target="_blank" rel="noopener noreferrer">
                ARM-Thinker：通过智能工具使用与视觉推理增强多模态生成式奖励模型
            </a>
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            ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shengyuan Ding, Xinyu Fang, Ziyu Liu, Yuhang Zang, Yuhang Cao, Xiangyu Zhao, Hao...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要涉及多模态生成模型和智能体工具使用，属于AIGC和内容生成领域，与您关注的推荐/搜索/广告核心领域进展、Transformer架构效率、或LLM在推荐系统的直接应用相关性较弱。虽然提到奖励模型可能间接涉及强化学习，但未明确指向推荐/搜索/广告场景，且视觉推理部分更偏向纯视觉应用而非异构数据处理。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05111v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05111v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to use tools for verification, limiting their reliability on complex multimodal reasoning tasks. We present ARM-Thinker, an A}gentic multimodal Reward Model that autonomously invokes external tools (e.g., image cropping, doc page retrieval) to ground judgments in verifiable evidence, replacing static, non-interactive reward scoring. This enables the model to verify fine-grained visual details, cross-reference multi-page evidence, and validate reasoning claims, which are capabilities absent in existing reward models. We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy. To evaluate agentic reward modeling, we introduce ARMBench-VL, comprising three benchmarks that assess fine-grained visual grounding (image-level tools), multi-page document understanding (retrieval tools), and instruction following (text-level verification). ARM-Thinker achieves +16.2% average improvement on reward modeling benchmarks, +9.6% on tool-use tasks, and outperforms baselines on multimodal math and logical reasoning benchmarks. Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.
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            <a href="https://www.alphaxiv.org/abs/2512.05098v1" target="_blank" rel="noopener noreferrer">
                SA-IQA：基于多维奖励的空间美学图像质量评估重新定义
            </a>
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            SA-IQA: Redefining Image Quality Assessment for Spatial Aesthetics with Multi-Dimensional Rewards
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuan Gao, Jin Song
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像质量评估(IQA)领域，特别是空间美学方面，这属于纯粹的计算机视觉研究方向。虽然图像质量在广告创意或内容推荐中可能间接相关，但论文标题明确聚焦于评估方法本身，没有表明与推荐系统、搜索或广告的排名、建模等核心任务有直接联系。该工作更接近视觉质量分析，而非您关注的LLM技术、Transformer架构进展或直接应用于RecSys/Search/Ads的模型。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:58:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05098v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    In recent years, Image Quality Assessment (IQA) for AI-generated images (AIGI) has advanced rapidly; however, existing methods primarily target portraits and artistic images, lacking a systematic evaluation of interior scenes. We introduce Spatial Aesthetics, a paradigm that assesses the aesthetic quality of interior images along four dimensions: layout, harmony, lighting, and distortion. We construct SA-BENCH, the first benchmark for spatial aesthetics, comprising 18,000 images and 50,000 precise annotations. Employing SA-BENCH, we systematically evaluate current IQA methodologies and develop SA-IQA, through MLLM fine-tuning and a multidimensional fusion approach, as a comprehensive reward framework for assessing spatial aesthetics. We apply SA-IQA to two downstream tasks: (1) serving as a reward signal integrated with GRPO reinforcement learning to optimize the AIGC generation pipeline, and (2) Best-of-N selection to filter high-quality images and improve generation quality. Experiments indicate that SA-IQA significantly outperforms existing methods on SA-BENCH, setting a new standard for spatial aesthetics evaluation. Code and dataset will be open-sourced to advance research and applications in this domain.
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            <a href="https://www.alphaxiv.org/abs/2512.05060v1" target="_blank" rel="noopener noreferrer">
                4DLangVGGT：基于4D语言-视觉几何的Transformer模型
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            4DLangVGGT: 4D Language-Visual Geometry Grounded Transformer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xianfeng Wu, Yajing Bai, Minghan Li, Xianzu Wu, Xueqi Zhao, Zhongyuan Lai, Wenyu...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及4D语言-视觉几何Transformer，主要属于视觉-语言多模态领域，与纯粹的推荐系统、搜索或广告技术关联较弱。虽然标题包含Transformer架构，但其核心是4D几何与视觉-语言结合，更偏向计算机视觉应用，而非推荐/搜索/广告领域的直接技术应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:15:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05060v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05060v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Constructing 4D language fields is crucial for embodied AI, augmented/virtual reality, and 4D scene understanding, as they provide enriched semantic representations of dynamic environments and enable open-vocabulary querying in complex scenarios. However, existing approaches to 4D semantic field construction primarily rely on scene-specific Gaussian splatting, which requires per-scene optimization, exhibits limited generalization, and is difficult to scale to real-world applications. To address these limitations, we propose 4DLangVGGT, the first Transformer-based feed-forward unified framework for 4D language grounding, that jointly integrates geometric perception and language alignment within a single architecture. 4DLangVGGT has two key components: the 4D Visual Geometry Transformer, StreamVGGT, which captures spatio-temporal geometric representations of dynamic scenes; and the Semantic Bridging Decoder (SBD), which projects geometry-aware features into a language-aligned semantic space, thereby enhancing semantic interpretability while preserving structural fidelity. Unlike prior methods that depend on costly per-scene optimization, 4DLangVGGT can be jointly trained across multiple dynamic scenes and directly applied during inference, achieving both deployment efficiency and strong generalization. This design significantly improves the practicality of large-scale deployment and establishes a new paradigm for open-vocabulary 4D scene understanding. Experiments on HyperNeRF and Neu3D datasets demonstrate that our approach not only generalizes effectively but also achieves state-of-the-art performance, achieving up to 2% gains under per-scene training and 1% improvements under multi-scene training. Our code released in https://github.com/hustvl/4DLangVGGT
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            <a href="https://www.alphaxiv.org/abs/2512.04952v1" target="_blank" rel="noopener noreferrer">
                FASTer：通过神经动作标记化实现高效自回归视觉语言动作建模
            </a>
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            FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via neural Action Tokenization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yicheng Liu, Shiduo Zhang, Zibin Dong, Baijun Ye, Tianyuan Yuan, Xiaopeng Yu, Li...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言动作建模，属于机器人或具身AI领域，与推荐系统、搜索或广告的核心焦点不直接相关。虽然提到了自回归建模和效率改进，但这些技术主要应用于视觉-语言-动作序列，而非推荐/搜索/广告中的用户行为序列或异构数据建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:21:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04952v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04952v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Autoregressive vision-language-action (VLA) models have recently demonstrated strong capabilities in robotic manipulation. However, their core process of action tokenization often involves a trade-off between reconstruction fidelity and inference efficiency. We introduce FASTer, a unified framework for efficient and generalizable robot learning that integrates a learnable tokenizer with an autoregressive policy built upon it. FASTerVQ encodes action chunks as single-channel images, capturing global spatio-temporal dependencies while maintaining a high compression ratio. FASTerVLA builds on this tokenizer with block-wise autoregressive decoding and a lightweight action expert, achieving both faster inference and higher task performance. Extensive experiments across simulated and real-world benchmarks show that FASTerVQ delivers superior reconstruction quality, high token utilization, and strong cross-task and cross-embodiment generalization, while FASTerVLA further improves overall capability, surpassing previous state-of-the-art VLA models in both inference speed and task performance.
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            <a href="https://www.alphaxiv.org/abs/2512.04943v1" target="_blank" rel="noopener noreferrer">
                面向自适应融合多模态深度网络的人类动作识别研究
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            Towards Adaptive Fusion of Multimodal Deep Networks for Human Action Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Novanto Yudistira
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多模态融合技术，这与VLM类比异构数据的思路有概念上的相似性，但具体应用领域是人类动作识别，属于计算机视觉范畴。虽然多模态融合方法在理论上可能启发推荐系统中处理异构特征（如用户序列、上下文信息），但论文标题明确指向特定视觉任务，缺乏对推荐/搜索/广告领域的直接应用潜力说明。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:09:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04943v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04943v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This study introduces a pioneering methodology for human action recognition by harnessing deep neural network techniques and adaptive fusion strategies across multiple modalities, including RGB, optical flows, audio, and depth information. Employing gating mechanisms for multimodal fusion, we aim to surpass limitations inherent in traditional unimodal recognition methods while exploring novel possibilities for diverse applications. Through an exhaustive investigation of gating mechanisms and adaptive weighting-based fusion architectures, our methodology enables the selective integration of relevant information from various modalities, thereby bolstering both accuracy and robustness in action recognition tasks. We meticulously examine various gated fusion strategies to pinpoint the most effective approach for multimodal action recognition, showcasing its superiority over conventional unimodal methods. Gating mechanisms facilitate the extraction of pivotal features, resulting in a more holistic representation of actions and substantial enhancements in recognition performance. Our evaluations across human action recognition, violence action detection, and multiple self-supervised learning tasks on benchmark datasets demonstrate promising advancements in accuracy. The significance of this research lies in its potential to revolutionize action recognition systems across diverse fields. The fusion of multimodal information promises sophisticated applications in surveillance and human-computer interaction, especially in contexts related to active assisted living.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04939v1" target="_blank" rel="noopener noreferrer">
                LiteVGGT：通过几何感知的缓存令牌合并增强基础VGGT
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            LiteVGGT: Boosting Vanilla VGGT via Geometry-aware Cached Token Merging
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhijian Shu, Cheng Lin, Tao Xie, Wei Yin, Ben Li, Zhiyuan Pu, Weize Li, Yao Yao,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题提到VGGT（可能指视觉几何组变换器）和令牌合并技术，这属于Transformer架构效率优化范畴，可能涉及注意力机制改进。然而，标题明确聚焦于视觉几何处理，没有表明在推荐系统、搜索或广告中的应用潜力，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:07:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04939v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04939v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    3D vision foundation models like Visual Geometry Grounded Transformer (VGGT) have advanced greatly in geometric perception. However, it is time-consuming and memory-intensive for long sequences, limiting application to large-scale scenes beyond hundreds of images. To address this, we propose LiteVGGT, achieving up to 10x speedup and substantial memory reduction, enabling efficient processing of 1000-image scenes. We derive two key insights for 3D reconstruction: (1) tokens from local image regions have inherent geometric correlations, leading to high similarity and computational redundancy; (2) token similarity across adjacent network layers remains stable, allowing for reusable merge decisions. Guided by these, we design a simple yet efficient strategy, dubbed geometry-aware cached token merging. We analyze each token's geometric importance, optimizing anchor token selection to better preserve key information for reconstruction. We also cache and reuse merge indices across layers, substantially reducing latency with minimal accuracy impact. This strategy retains VGGT's core performance, enabling efficient fine-tuning and FP8 quantization for further gains. Extensive experiments validate LiteVGGT's effectiveness, scalability, and robustness. Project page: https://garlicba.github.io/LiteVGGT/
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            <a href="https://www.alphaxiv.org/abs/2512.04926v1" target="_blank" rel="noopener noreferrer">
                语义引领方向：通过异步潜在扩散实现语义与纹理建模的协调
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yueming Pan, Ruoyu Feng, Qi Dai, Yuqi Wang, Wenfeng Lin, Mingyu Guo, Chong Luo, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于语义与纹理建模的协调，属于计算机视觉领域的生成模型技术，与视觉-语言模型(VLM)的异构数据处理理念有间接联系。然而，标题未明确提及推荐系统、搜索或广告应用，且缺乏对Transformer架构或LLM技术的直接关联，因此潜在应用不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:57:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04926v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04926v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. This indicates the preceding semantics potentially benefit texture generation by providing a semantic anchor. Recent advances have integrated semantic priors from pretrained visual encoders to further enhance LDMs, yet they still denoise semantic and VAE-encoded texture synchronously, neglecting such ordering. Observing these, we propose Semantic-First Diffusion (SFD), a latent diffusion paradigm that explicitly prioritizes semantic formation. SFD first constructs composite latents by combining a compact semantic latent, which is extracted from a pretrained visual encoder via a dedicated Semantic VAE, with the texture latent. The core of SFD is to denoise the semantic and texture latents asynchronously using separate noise schedules: semantics precede textures by a temporal offset, providing clearer high-level guidance for texture refinement and enabling natural coarse-to-fine generation. On ImageNet 256x256 with guidance, SFD achieves FID 1.06 (LightningDiT-XL) and FID 1.04 (1.0B LightningDiT-XXL), while achieving up to 100x faster convergence than the original DiT. SFD also improves existing methods like ReDi and VA-VAE, demonstrating the effectiveness of asynchronous, semantics-led modeling. Project page and code: https://yuemingpan.github.io/SFD.github.io/.
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            <a href="https://www.alphaxiv.org/abs/2512.04904v1" target="_blank" rel="noopener noreferrer">
                ReflexFlow：重新思考流匹配中缓解曝光偏差的学习目标
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            ReflexFlow: Rethinking Learning Objective for Exposure Bias Alleviation in Flow Matching
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guanbo Huang, Jingjia Mao, Fanding Huang, Fengkai Liu, Xiangyang Luo, Yaoyuan Li...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注流匹配中的曝光偏差问题，这属于生成模型的技术范畴，与推荐系统、搜索或广告中的核心排序、检索或建模问题没有直接关联。虽然曝光偏差概念在推荐系统中存在，但论文聚焦于流匹配这一特定生成框架，其潜在应用更偏向AIGC或内容生成领域，而非您关注的直接应用或使能技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:34:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04904v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04904v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite tremendous recent progress, Flow Matching methods still suffer from exposure bias due to discrepancies in training and inference. This paper investigates the root causes of exposure bias in Flow Matching, including: (1) the model lacks generalization to biased inputs during training, and (2) insufficient low-frequency content captured during early denoising, leading to accumulated bias. Based on these insights, we propose ReflexFlow, a simple and effective reflexive refinement of the Flow Matching learning objective that dynamically corrects exposure bias. ReflexFlow consists of two components: (1) Anti-Drift Rectification (ADR), which reflexively adjusts prediction targets for biased inputs utilizing a redesigned loss under training-time scheduled sampling; and (2) Frequency Compensation (FC), which reflects on missing low-frequency components and compensates them by reweighting the loss using exposure bias. ReflexFlow is model-agnostic, compatible with all Flow Matching frameworks, and improves generation quality across datasets. Experiments on CIFAR-10, CelebA-64, and ImageNet-256 show that ReflexFlow outperforms prior approaches in mitigating exposure bias, achieving a 35.65% reduction in FID on CelebA-64.
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            <a href="https://www.alphaxiv.org/abs/2512.04857v1" target="_blank" rel="noopener noreferrer">
                自回归图像生成仅需少量缓存标记
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Autoregressive Image Generation Needs Only a Few Lines of Cached Tokens
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziran Qin, Youru Lv, Mingbao Lin, Zeren Zhang, Chanfan Gan, Tieyuan Chen, Weiyao...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及图像生成技术，属于AIGC/内容生成领域，与我的核心关注点（推荐系统、搜索、广告）无直接关联。虽然提到了缓存标记可能涉及效率优化，但未明确展示在推荐/搜索/广告场景中的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:41:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04857v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04857v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Autoregressive (AR) visual generation has emerged as a powerful paradigm for image and multimodal synthesis, owing to its scalability and generality. However, existing AR image generation suffers from severe memory bottlenecks due to the need to cache all previously generated visual tokens during decoding, leading to both high storage requirements and low throughput. In this paper, we introduce \textbf{LineAR}, a novel, training-free progressive key-value (KV) cache compression pipeline for autoregressive image generation. By fully exploiting the intrinsic characteristics of visual attention, LineAR manages the cache at the line level using a 2D view, preserving the visual dependency regions while progressively evicting less-informative tokens that are harmless for subsequent line generation, guided by inter-line attention. LineAR enables efficient autoregressive (AR) image generation by utilizing only a few lines of cache, achieving both memory savings and throughput speedup, while maintaining or even improving generation quality. Extensive experiments across six autoregressive image generation models, including class-conditional and text-to-image generation, validate its effectiveness and generality. LineAR improves ImageNet FID from 2.77 to 2.68 and COCO FID from 23.85 to 22.86 on LlamaGen-XL and Janus-Pro-1B, while retaining only 1/6 KV cache. It also improves DPG on Lumina-mGPT-768 with just 1/8 KV cache. Additionally, LineAR achieves significant memory and throughput gains, including up to 67.61% memory reduction and 7.57x speedup on LlamaGen-XL, and 39.66% memory reduction and 5.62x speedup on Janus-Pro-7B.
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            <a href="https://www.alphaxiv.org/abs/2512.04832v1" target="_blank" rel="noopener noreferrer">
                建筑标记化：用于布局合成的Transformer模型
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Tokenizing Buildings: A Transformer for Layout Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Manuel Ladron de Guevara, Jinmo Rhee, Ardavan Bidgoli, Vaidas Razgaitis, Michael...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及将建筑布局视为标记序列并使用Transformer进行合成，这属于计算机图形学或建筑设计的特定领域应用。虽然提到了Transformer架构，但缺乏与推荐系统、搜索或广告的明确关联，且布局合成与异构数据处理或VLM类比没有直接联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:16:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04832v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04832v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span><span class="category-tag">cs.LG</span></div>
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                    We introduce Small Building Model (SBM), a Transformer-based architecture for layout synthesis in Building Information Modeling (BIM) scenes. We address the question of how to tokenize buildings by unifying heterogeneous feature sets of architectural elements into sequences while preserving compositional structure. Such feature sets are represented as a sparse attribute-feature matrix that captures room properties. We then design a unified embedding module that learns joint representations of categorical and possibly correlated continuous feature groups. Lastly, we train a single Transformer backbone in two modes: an encoder-only pathway that yields high-fidelity room embeddings, and an encoder-decoder pipeline for autoregressive prediction of room entities, referred to as Data-Driven Entity Prediction (DDEP). Experiments across retrieval and generative layout synthesis show that SBM learns compact room embeddings that reliably cluster by type and topology, enabling strong semantic retrieval. In DDEP mode, SBM produces functionally sound layouts, with fewer collisions and boundary violations and improved navigability.
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            <a href="https://www.alphaxiv.org/abs/2512.04830v1" target="_blank" rel="noopener noreferrer">
                FreeGen：用于自由视角驾驶场景合成的前馈重建-生成协同训练
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        <div class="mb-2 text-base text-gray-700">
            FreeGen: Feed-Forward Reconstruction-Generation Co-Training for Free-Viewpoint Driving Scene Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shijie Chen, Peixi Peng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的驾驶场景合成和自由视角生成，属于3D视觉和图形学领域。虽然标题提到了“生成”，但这与推荐系统、搜索或广告中的内容生成应用不同，且没有明确涉及LLM技术或Transformer架构。它更偏向纯粹的视觉任务，与当前关注的领域相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:14:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04830v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04830v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Closed-loop simulation and scalable pre-training for autonomous driving require synthesizing free-viewpoint driving scenes. However, existing datasets and generative pipelines rarely provide consistent off-trajectory observations, limiting large-scale evaluation and training. While recent generative models demonstrate strong visual realism, they struggle to jointly achieve interpolation consistency and extrapolation realism without per-scene optimization. To address this, we propose FreeGen, a feed-forward reconstruction-generation co-training framework for free-viewpoint driving scene synthesis. The reconstruction model provides stable geometric representations to ensure interpolation consistency, while the generation model performs geometry-aware enhancement to improve realism at unseen viewpoints. Through co-training, generative priors are distilled into the reconstruction model to improve off-trajectory rendering, and the refined geometry in turn offers stronger structural guidance for generation. Experiments demonstrate that FreeGen achieves state-of-the-art performance for free-viewpoint driving scene synthesis.
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            <a href="https://www.alphaxiv.org/abs/2512.04678v1" target="_blank" rel="noopener noreferrer">
                奖励强制：通过奖励分布匹配蒸馏实现高效的流式视频生成
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
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        <div class="mb-2 text-base text-gray-700">
            Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunhong Lu, Yanhong Zeng, Haobo Li, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Jiap...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频生成技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然提到了蒸馏技术，但其应用场景仅限于视频生成，没有展示在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:12:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04678v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04678v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Efficient streaming video generation is critical for simulating interactive and dynamic worlds. Existing methods distill few-step video diffusion models with sliding window attention, using initial frames as sink tokens to maintain attention performance and reduce error accumulation. However, video frames become overly dependent on these static tokens, resulting in copied initial frames and diminished motion dynamics. To address this, we introduce Reward Forcing, a novel framework with two key designs. First, we propose EMA-Sink, which maintains fixed-size tokens initialized from initial frames and continuously updated by fusing evicted tokens via exponential moving average as they exit the sliding window. Without additional computation cost, EMA-Sink tokens capture both long-term context and recent dynamics, preventing initial frame copying while maintaining long-horizon consistency. Second, to better distill motion dynamics from teacher models, we propose a novel Rewarded Distribution Matching Distillation (Re-DMD). Vanilla distribution matching treats every training sample equally, limiting the model's ability to prioritize dynamic content. Instead, Re-DMD biases the model's output distribution toward high-reward regions by prioritizing samples with greater dynamics rated by a vision-language model. Re-DMD significantly enhances motion quality while preserving data fidelity. We include both quantitative and qualitative experiments to show that Reward Forcing achieves state-of-the-art performance on standard benchmarks while enabling high-quality streaming video generation at 23.1 FPS on a single H100 GPU.
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            <a href="https://www.alphaxiv.org/abs/2512.04619v1" target="_blank" rel="noopener noreferrer">
                去噪以追踪：利用视频扩散先验实现鲁棒对应关系
            </a>
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            Denoise to Track: Harnessing Video Diffusion Priors for Robust Correspondence
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianyu Yuan, Yuanbo Yang, Lin-Zhuo Chen, Yao Yao, Zhuzhong Qian
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于视频处理和计算机视觉中的对应关系追踪，主要涉及扩散模型在视频领域的应用。虽然扩散模型属于生成式AI技术，但该论文明确针对视频模态，没有表明其方法或技术可以推广到推荐系统、搜索或广告领域所需的异构数据（如用户序列、上下文特征）处理。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 09:48:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04619v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04619v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this work, we introduce HeFT (Head-Frequency Tracker), a zero-shot point tracking framework that leverages the visual priors of pretrained video diffusion models. To better understand how they encode spatiotemporal information, we analyze the internal representations of Video Diffusion Transformer (VDiT). Our analysis reveals that attention heads act as minimal functional units with distinct specializations for matching, semantic understanding, and positional encoding. Additionally, we find that the low-frequency components in VDiT features are crucial for establishing correspondences, whereas the high-frequency components tend to introduce noise. Building on these insights, we propose a head- and frequency-aware feature selection strategy that jointly selects the most informative attention head and low-frequency components to enhance tracking performance. Specifically, our method extracts discriminative features through single-step denoising, applies feature selection, and employs soft-argmax localization with forward-backward consistency checks for correspondence estimation. Extensive experiments on TAP-Vid benchmarks demonstrate that HeFT achieves state-of-the-art zero-shot tracking performance, approaching the accuracy of supervised methods while eliminating the need for annotated training data. Our work further underscores the promise of video diffusion models as powerful foundation models for a wide range of downstream tasks, paving the way toward unified visual foundation models.
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            <a href="https://www.alphaxiv.org/abs/2512.04585v1" target="_blank" rel="noopener noreferrer">
                SAM3-I：基于指令的通用分割模型
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            SAM3-I: Segment Anything with Instructions
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Yongri Piao, Qi Bi, Miao Zhang,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于视觉分割任务（Segment Anything），属于纯粹的计算机视觉领域。虽然“基于指令”可能暗示与语言模型的交互，但核心内容与推荐系统、搜索或广告的排名、建模等关键技术需求无直接关联。视觉分割技术主要应用于图像理解、编辑等场景，在您关注的领域缺乏明确的应用路径。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 09:00:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04585v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04585v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Segment Anything Model 3 (SAM3) has advanced open-vocabulary segmentation through promptable concept segmentation, allowing users to segment all instances corresponding to a given concept, typically specified with short noun-phrase (NP) prompts. While this marks the first integration of language-level concepts within the SAM family, real-world usage typically requires far richer expressions that include attributes, spatial relations, functionalities, actions, states, and even implicit reasoning over instances. Currently, SAM3 relies on external multi-modal agents to convert complex instructions into NPs and then conduct iterative mask filtering. However, these NP-level concepts remain overly coarse, often failing to precisely represent a specific instance. In this work, we present SAM3-I, an enhanced framework that unifies concept-level understanding and instruction-level reasoning within the SAM family. SAM3-I introduces an instruction-aware cascaded adaptation mechanism that progressively aligns expressive instruction semantics with SAM3's existing vision-language representations, enabling direct instruction-following segmentation without sacrificing its original concept-driven capabilities. Furthermore, we design a structured instruction taxonomy spanning concept, simple, and complex levels, and develop a scalable data engine to construct a dataset with diverse instruction-mask pairs. Experiments show that SAM3-I delivers appealing performance, demonstrating that SAM3 can be effectively extended to follow natural-language instructions while preserving its strong concept grounding. We open-source SAM3-I and provide practical fine-tuning workflows, enabling researchers to adapt it to domain-specific applications. The source code is available here.
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            <a href="https://www.alphaxiv.org/abs/2512.04581v1" target="_blank" rel="noopener noreferrer">
                基于动态特征精炼与全局上下文注意力知识蒸馏的红外无人机目标跟踪
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        <div class="mb-2 text-base text-gray-700">
            Infrared UAV Target Tracking with Dynamic Feature Refinement and Global Contextual Attention Knowledge Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Houzhang Fang, Chenxing Wu, Kun Bai, Tianqi Chen, Xiaolin Wang, Xiyang Liu, Yi C...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注红外无人机目标跟踪，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术关联较弱。尽管涉及注意力机制，但其应用场景（红外无人机跟踪）与RecSys/Search/Ads的典型数据模态（用户行为、文本、商品特征等）差异显著，潜在应用价值有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:49:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04581v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04581v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Unmanned aerial vehicle (UAV) target tracking based on thermal infrared imaging has been one of the most important sensing technologies in anti-UAV applications. However, the infrared UAV targets often exhibit weak features and complex backgrounds, posing significant challenges to accurate tracking. To address these problems, we introduce SiamDFF, a novel dynamic feature fusion Siamese network that integrates feature enhancement and global contextual attention knowledge distillation for infrared UAV target (IRUT) tracking. The SiamDFF incorporates a selective target enhancement network (STEN), a dynamic spatial feature aggregation module (DSFAM), and a dynamic channel feature aggregation module (DCFAM). The STEN employs intensity-aware multi-head cross-attention to adaptively enhance important regions for both template and search branches. The DSFAM enhances multi-scale UAV target features by integrating local details with global features, utilizing spatial attention guidance within the search frame. The DCFAM effectively integrates the mixed template generated from STEN in the template branch and original template, avoiding excessive background interference with the template and thereby enhancing the emphasis on UAV target region features within the search frame. Furthermore, to enhance the feature extraction capabilities of the network for IRUT without adding extra computational burden, we propose a novel tracking-specific target-aware contextual attention knowledge distiller. It transfers the target prior from the teacher network to the student model, significantly improving the student network's focus on informative regions at each hierarchical level of the backbone network. Extensive experiments on real infrared UAV datasets demonstrate that the proposed approach outperforms state-of-the-art target trackers under complex backgrounds while achieving a real-time tracking speed.
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            <a href="https://www.alphaxiv.org/abs/2512.04568v1" target="_blank" rel="noopener noreferrer">
                Prompt2Craft：利用大型语言模型生成功能性工艺组件
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Prompt2Craft: Generating Functional Craft Assemblies with LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vitor Hideyo Isume, Takuya Kiyokawa, Natsuki Yamanobe, Yukiyasu Domae, Weiwei Wa...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于使用LLMs进行工艺组件的生成，这属于AIGC/内容生成领域，与您关注的推荐系统、搜索或广告中的直接应用无关。虽然涉及LLMs，但其应用场景（工艺组件生成）与您指定的核心领域（RecSys/Search/Ads）没有明显关联，且可能偏向于纯粹的LLM内容生成任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:32:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04568v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04568v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Inspired by traditional handmade crafts, where a person improvises assemblies based on the available objects, we formally introduce the Craft Assembly Task. It is a robotic assembly task that involves building an accurate representation of a given target object using the available objects, which do not directly correspond to its parts. In this work, we focus on selecting the subset of available objects for the final craft, when the given input is an RGB image of the target in the wild. We use a mask segmentation neural network to identify visible parts, followed by retrieving labeled template meshes. These meshes undergo pose optimization to determine the most suitable template. Then, we propose to simplify the parts of the transformed template mesh to primitive shapes like cuboids or cylinders. Finally, we design a search algorithm to find correspondences in the scene based on local and global proportions. We develop baselines for comparison that consider all possible combinations, and choose the highest scoring combination for common metrics used in foreground maps and mask accuracy. Our approach achieves comparable results to the baselines for two different scenes, and we show qualitative results for an implementation in a real-world scenario.
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            <a href="https://www.alphaxiv.org/abs/2512.04563v1" target="_blank" rel="noopener noreferrer">
                COOPER：空间智能中协同感知与推理的统一模型
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            COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zefeng Zhang, Xiangzhao Hao, Hengzhu Tang, Zhenyu Zhang, Jiawei Sheng, Xiaodong ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及空间智能和协同感知，这主要属于机器人学、自动驾驶或计算机视觉领域，而非推荐系统、搜索或广告的核心技术。虽然“统一模型”和“推理”可能暗示多模态建模，但缺乏与异构用户数据、序列建模或Transformer架构改进的直接联系，因此对当前关注点的潜在应用不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:26:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04563v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04563v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Visual Spatial Reasoning is crucial for enabling Multimodal Large Language Models (MLLMs) to understand object properties and spatial relationships, yet current models still struggle with 3D-aware reasoning. Existing approaches typically enhance either perception, by augmenting RGB inputs with auxiliary modalities such as depth and segmentation, or reasoning, by training on spatial VQA datasets and applying reinforcement learning, and thus treat these two aspects in isolation. In this work, we investigate whether a unified MLLM can develop an intrinsic ability to enhance spatial perception and, through adaptive interleaved reasoning, achieve stronger spatial intelligence. We propose \textbf{COOPER}, a unified MLLM that leverages depth and segmentation as auxiliary modalities and is trained in two stages to acquire auxiliary modality generation and adaptive, interleaved reasoning capabilities. COOPER achieves an average \textbf{6.91\%} improvement in spatial reasoning while maintaining general performance. Moreover, even a variant trained only for auxiliary modality generation attains a \textbf{7.92\%} gain on distance and size estimation, suggesting that learning to generate auxiliary modalities helps internalize spatial knowledge and strengthen spatial understanding.
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            <a href="https://www.alphaxiv.org/abs/2512.04554v1" target="_blank" rel="noopener noreferrer">
                伪造答案：针对无OCR文档视觉问答的对抗性伪造攻击
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Counterfeit Answers: Adversarial Forgery against OCR-Free Document Visual Question Answering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marco Pintore, Maura Pintor, Dimosthenis Karatzas, Battista Biggio
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文档视觉问答中的对抗攻击和伪造问题，属于计算机视觉和安全交叉领域。虽然文档理解在搜索中有潜在应用，但论文的核心焦点是攻击方法和安全漏洞，这直接落入您指定的无关主题（安全、隐私），且没有明确展示在推荐系统、搜索或广告中的实际应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:15:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04554v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04554v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Document Visual Question Answering (DocVQA) enables end-to-end reasoning grounded on information present in a document input. While recent models have shown impressive capabilities, they remain vulnerable to adversarial attacks. In this work, we introduce a novel attack scenario that aims to forge document content in a visually imperceptible yet semantically targeted manner, allowing an adversary to induce specific or generally incorrect answers from a DocVQA model. We develop specialized attack algorithms that can produce adversarially forged documents tailored to different attackers' goals, ranging from targeted misinformation to systematic model failure scenarios. We demonstrate the effectiveness of our approach against two end-to-end state-of-the-art models: Pix2Struct, a vision-language transformer that jointly processes image and text through sequence-to-sequence modeling, and Donut, a transformer-based model that directly extracts text and answers questions from document images. Our findings highlight critical vulnerabilities in current DocVQA systems and call for the development of more robust defenses.
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            <a href="https://www.alphaxiv.org/abs/2512.04540v1" target="_blank" rel="noopener noreferrer">
                VideoMem：通过自适应内存管理增强超长视频理解
            </a>
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            VideoMem: Enhancing Ultra-Long Video Understanding via Adaptive Memory Management
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongbo Jin, Qingyuan Wang, Wenhao Zhang, Yang Liu, Sijie Cheng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频理解领域，属于纯粹的视觉处理任务，与推荐系统、搜索或广告的核心技术焦点没有直接关联。虽然内存管理技术可能具有通用性，但论文标题未表明其在异构数据处理或推荐/搜索应用中的潜在价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:42:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04540v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04540v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Ultra long video understanding remains an open challenge, as existing vision language models (VLMs) falter on such content due to limited context length and inefficient long term memory retention. To address this, recent works have attempted to construct external knowledge bases and corresponding retrieval agumented generation (RAG) systems, yet these incur enormous storage and computational overhead. In this paper, we propose VideoMem, a novel framework that pioneers models long video understanding as a sequential generation task via adaptive memory management. Specifically, VideoMem dynamically updates a global memory buffer, which adaptively retains critical information while discarding redundant content across the video timeline. To efficiently train VLMs for such long-term tasks, VideoMem integrates the Progressive Grouped Relative Policy Optimization (PRPO) algorithm, equipped with two core modules: Progressive State Propagation (PSP) adaptively retains valid current states, propagates them to the next rollout step, and gradually narrows the model exploration space. Temporal Cascading Reward (TCR) further alleviates reward sparsity, improving sample utilization and accelerating convergence. Extensive experiments demonstrate that VideoMem significantly outperforms existing open-source models across diverse benchmarks for ultra-long video understanding tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.04532v1" target="_blank" rel="noopener noreferrer">
                PhyVLLM：基于物理引导的视频语言模型，具有运动-外观解耦特性
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        <div class="mb-2 text-base text-gray-700">
            PhyVLLM: Physics-Guided Video Language Model with Motion-Appearance Disentanglement
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu-Wei Zhan, Xin Wang, Hong Chen, Tongtong Feng, Wei Feng, Ren Wang, Guangyao Li...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频语言模型和物理引导，属于视觉-语言交叉领域，但未明确展示与推荐系统、搜索或广告的直接关联。虽然VLM类比可能提供异构数据处理思路，但论文标题强调视频特定任务（运动-外观解耦），缺乏明确的RecSys/Search/Ads应用潜力说明。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:28:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04532v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04532v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video Large Language Models (Video LLMs) have shown impressive performance across a wide range of video-language tasks. However, they often fail in scenarios requiring a deeper understanding of physical dynamics. This limitation primarily arises from their reliance on appearance-based matching. Incorporating physical motion modeling is crucial for deeper video understanding, but presents three key challenges: (1) motion signals are often entangled with appearance variations, making it difficult to extract clean physical cues; (2) effective motion modeling requires not only continuous-time motion representations but also capturing physical dynamics; and (3) collecting accurate annotations for physical attributes is costly and often impractical. To address these issues, we propose PhyVLLM, a physical-guided video-language framework that explicitly incorporates physical motion into Video LLMs. Specifically, PhyVLLM disentangles visual appearance and object motion through a dual-branch encoder. To model physical dynamics over time, we incorporate a Neural Ordinary Differential Equation (Neural ODE) module, which generates differentiable physical dynamic representations. The resulting motion-aware representations are projected into the token space of a pretrained LLM, enabling physics reasoning without compromising the model's original multimodal capabilities. To circumvent the need for explicit physical labels, PhyVLLM employs a self-supervised manner to model the continuous evolution of object motion. Experimental results demonstrate that PhyVLLM significantly outperforms state-of-the-art Video LLMs on both physical reasoning and general video understanding tasks, highlighting the advantages of incorporating explicit physical modeling.
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            <a href="https://www.alphaxiv.org/abs/2512.04522v1" target="_blank" rel="noopener noreferrer">
                可见光-红外行人重识别中的身份线索精炼与增强
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Identity Clue Refinement and Enhancement for Visible-Infrared Person Re-Identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guoqing Zhang, Zhun Wang, Hairui Wang, Zhonglin Ye, Yuhui Zheng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于跨模态行人重识别，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然涉及多模态数据处理，但其应用场景（安防监控）和技术方法（图像特征匹配）与您关注的LLM在推荐/搜索/广告中的应用方向不符。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:13:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04522v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04522v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                    Visible-Infrared Person Re-Identification (VI-ReID) is a challenging cross-modal matching task due to significant modality discrepancies. While current methods mainly focus on learning modality-invariant features through unified embedding spaces, they often focus solely on the common discriminative semantics across modalities while disregarding the critical role of modality-specific identity-aware knowledge in discriminative feature learning. To bridge this gap, we propose a novel Identity Clue Refinement and Enhancement (ICRE) network to mine and utilize the implicit discriminative knowledge inherent in modality-specific attributes. Initially, we design a Multi-Perception Feature Refinement (MPFR) module that aggregates shallow features from shared branches, aiming to capture modality-specific attributes that are easily overlooked. Then, we propose a Semantic Distillation Cascade Enhancement (SDCE) module, which distills identity-aware knowledge from the aggregated shallow features and guide the learning of modality-invariant features. Finally, an Identity Clues Guided (ICG) Loss is proposed to alleviate the modality discrepancies within the enhanced features and promote the learning of a diverse representation space. Extensive experiments across multiple public datasets clearly show that our proposed ICRE outperforms existing SOTA methods.
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            <a href="https://www.alphaxiv.org/abs/2512.04483v1" target="_blank" rel="noopener noreferrer">
                DeRA：用于视频标记化的解耦表示对齐方法
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            DeRA: Decoupled Representation Alignment for Video Tokenization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pengbo Guo, Junke Wang, Zhen Xing, Chengxu Liu, Daoguo Dong, Xueming Qian, Zuxua...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及视频标记化，属于计算机视觉领域，与RecSys/Search/Ads的核心关注点（如用户行为序列、上下文特征、推荐排序等）没有直接关联。尽管视频内容可能出现在某些推荐场景中，但该论文的技术焦点是视频表示学习，而非针对RecSys/Search/Ads的特定应用或架构创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:37:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04483v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04483v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper presents DeRA, a novel 1D video tokenizer that decouples the spatial-temporal representation learning in video tokenization to achieve better training efficiency and performance. Specifically, DeRA maintains a compact 1D latent space while factorizing video encoding into appearance and motion streams, which are aligned with pretrained vision foundation models to capture the spatial semantics and temporal dynamics in videos separately. To address the gradient conflicts introduced by the heterogeneous supervision, we further propose the Symmetric Alignment-Conflict Projection (SACP) module that proactively reformulates gradients by suppressing the components along conflicting directions. Extensive experiments demonstrate that DeRA outperforms LARP, the previous state-of-the-art video tokenizer by 25% on UCF-101 in terms of rFVD. Moreover, using DeRA for autoregressive video generation, we also achieve new state-of-the-art results on both UCF-101 class-conditional generation and K600 frame prediction.
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            <a href="https://www.alphaxiv.org/abs/2512.04852v1" target="_blank" rel="noopener noreferrer">
                安全提问：面向知识图谱的隐私感知大语言模型查询生成
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Ask Safely: Privacy-Aware LLM Query Generation for Knowledge Graphs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mauro Dalle Lucca Tosi, Jordi Cabot
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及隐私（Privacy-Aware），这属于明确排除的无关主题（Irrelevant Topics）。虽然涉及LLM和知识图谱，但核心关注点是隐私保护而非推荐/搜索/广告领域的技术进展或应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:37:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04852v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04852v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                    Large Language Models (LLMs) are increasingly used to query knowledge graphs (KGs) due to their strong semantic understanding and extrapolation capabilities compared to traditional approaches. However, these methods cannot be applied when the KG contains sensitive data and the user lacks the resources to deploy a local generative LLM. To address this issue, we propose a privacy-aware query generation approach for KGs. Our method identifies sensitive information in the graph based on its structure and omits such values before requesting the LLM to translate natural language questions into Cypher queries. Experimental results show that our approach preserves the quality of the generated queries while preventing sensitive data from being transmitted to third-party services.
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            <a href="https://www.alphaxiv.org/abs/2512.05112v1" target="_blank" rel="noopener noreferrer">
                DraCo：将草稿作为思维链用于文本到图像预览与罕见概念生成
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            DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dongzhi Jiang, Renrui Zhang, Haodong Li, Zhuofan Zong, Ziyu Guo, Jun He, Claire ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于文本到图像生成（Text-to-Image），属于纯粹的AIGC/内容生成领域，与推荐系统、搜索或广告的排序/检索核心任务无关。标题中提到的“思维链”技术在此上下文中服务于图像生成预览，而非推荐/搜索场景中的推理或解释需求，因此完全不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05112v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05112v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Recent unified multimodal large language models (MLLMs) have shown impressive capabilities, incorporating chain-of-thought (CoT) reasoning for enhanced text-to-image generation. However, existing approaches remain limited, either treating the model merely as a standalone generator or relying on abstract textual planning. To this end, we propose Draft-as-CoT (DraCo), a novel interleaved reasoning paradigm that fully leverages both textual and visual contents in CoT for better planning and verification. Our method first generates a low-resolution draft image as preview, providing more concrete and structural visual planning and guidance. Then, we employ the model's inherent understanding capability to verify potential semantic misalignments between the draft and input prompt, and performs refinement through selective corrections with super-resolution. In this way, our approach addresses two fundamental challenges: the coarse-grained nature of textual planning and the difficulty in generating rare attribute combinations. To support training, we curate DraCo-240K, aiming to enhance three atomic capabilities spanning general correction, instance manipulation, and layout reorganization. Supported by DraCo-CFG, a specialized classifier-free guidance (CFG) strategy for interleaved reasoning, DraCo achieves a tremendous increase on GenEval (+8%), Imagine-Bench (+0.91), and GenEval++ (+3%), significantly outperforming direct generation and other generation methods empowered by CoT.
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            <a href="https://www.alphaxiv.org/abs/2512.05066v1" target="_blank" rel="noopener noreferrer">
                多LLM协作用于药物推荐
            </a>
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            Multi-LLM Collaboration for Medication Recommendation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huascar Sanchez, Briland Hitaj, Jules Bergmann, Linda Briesemeister
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医疗领域的药物推荐应用，这属于明确的无关主题（Medical domain-specific application）。虽然涉及推荐系统，但医疗领域的特定应用与您关注的搜索、推荐、广告核心领域无关，且未表明包含对RecSys/Search/Ads有潜在价值的通用LLM技术或架构进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:25:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05066v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05066v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    As healthcare increasingly turns to AI for scalable and trustworthy clinical decision support, ensuring reliability in model reasoning remains a critical challenge. Individual large language models (LLMs) are susceptible to hallucinations and inconsistency, whereas naive ensembles of models often fail to deliver stable and credible recommendations. Building on our previous work on LLM Chemistry, which quantifies the collaborative compatibility among LLMs, we apply this framework to improve the reliability in medication recommendation from brief clinical vignettes. Our approach leverages multi-LLM collaboration guided by Chemistry-inspired interaction modeling, enabling ensembles that are effective (exploiting complementary strengths), stable (producing consistent quality), and calibrated (minimizing interference and error amplification). We evaluate our Chemistry-based Multi-LLM collaboration strategy on real-world clinical scenarios to investigate whether such interaction-aware ensembles can generate credible, patient-specific medication recommendations. Preliminary results are encouraging, suggesting that LLM Chemistry-guided collaboration may offer a promising path toward reliable and trustworthy AI assistants in clinical practice.
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            <a href="https://www.alphaxiv.org/abs/2512.04949v1" target="_blank" rel="noopener noreferrer">
                CARL：面向多步智能体的关键动作聚焦强化学习
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            CARL: Critical Action Focused Reinforcement Learning for Multi-Step Agent
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leyang Shen, Yang Zhang, Chun Kai Ling, Xiaoyan Zhao, Tat-Seng Chua
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于强化学习（RL）方法，但未提及与推荐系统、搜索或广告的任何具体关联。根据您的关注点排除标准，除非明确展示与RecSys/Search/Ads的相关性，否则RL论文被视为不相关。标题中缺乏此类连接，因此无法推断其潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:15:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04949v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04949v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Agents capable of accomplishing complex tasks through multiple interactions with the environment have emerged as a popular research direction. However, in such multi-step settings, the conventional group-level policy optimization algorithm becomes suboptimal because of its underlying assumption that each action holds equal contribution, which deviates significantly from reality. Our analysis reveals that only a small fraction of actions are critical in determining the final outcome. Building on this insight, we propose CARL, a critical-action-focused reinforcement learning algorithm tailored for multi-step agents. CARL achieves focused training through providing action-level optimization signals for high-criticality actions while excluding low-criticality actions from model update. Extensive experiments demonstrate that CARL achieves both stronger performance and higher efficiency during training and inference across diverse evaluation settings.
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            <a href="https://www.alphaxiv.org/abs/2512.04923v1" target="_blank" rel="noopener noreferrer">
                算法思维理论
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            Algorithmic Thinking Theory
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>MohammadHossein Bateni, Vincent Cohen-Addad, Yuzhou Gu, Silvio Lattanzi, Simon M...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题过于宽泛和理论化，未明确涉及推荐系统、搜索或广告领域的核心进展、LLM技术、Transformer架构改进或直接应用。标题缺乏具体的技术内容或应用方向，属于纯粹的理论研究，不符合当前关注的任何技术领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:55:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04923v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04923v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Large language models (LLMs) have proven to be highly effective for solving complex reasoning tasks. Surprisingly, their capabilities can often be improved by iterating on previously generated solutions. In this context, a reasoning plan for generating and combining a set of solutions can be thought of as an algorithm for reasoning using a probabilistic oracle. We introduce a theoretical framework for analyzing such reasoning algorithms. This framework formalizes the principles underlying popular techniques for iterative improvement and answer aggregation, providing a foundation for designing a new generation of more powerful reasoning methods. Unlike approaches for understanding models that rely on architectural specifics, our model is grounded in experimental evidence. As a result, it offers a general perspective that may extend to a wide range of current and future reasoning oracles.
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            <a href="https://www.alphaxiv.org/abs/2512.04921v1" target="_blank" rel="noopener noreferrer">
                人工智能消费者指数（ACE）
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            The AI Consumer Index (ACE)
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Julien Benchek, Rohit Shetty, Benjamin Hunsberger, Ajay Arun, Zach Richards, Bre...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题描述的是一个指数或指标系统，而非RecSys/Search/Ads领域的技术进展或LLM应用。它可能涉及消费者行为分析，但未明确指向核心推荐系统、搜索算法、广告技术或LLM/Transformer架构的进步。根据您的关注点，这属于无关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:54:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04921v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04921v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.HC</span></div>
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                    We introduce the first version of the AI Consumer Index (ACE), a benchmark for assessing whether frontier AI models can perform high-value consumer tasks. ACE contains a hidden heldout set of 400 test cases, split across four consumer activities: shopping, food, gaming, and DIY. We are also open sourcing 80 cases as a devset with a CC-BY license. For the ACE leaderboard we evaluated 10 frontier models (with websearch turned on) using a novel grading methodology that dynamically checks whether relevant parts of the response are grounded in the retrieved web sources. GPT 5 (Thinking = High) is the top-performing model, scoring 56.1%, followed by o3 Pro (Thinking = On) (55.2%) and GPT 5.1 (Thinking = High) (55.1%). Models differ across domains, and in Shopping the top model scores under 50%. For some requests (such as giving the correct price or providing working links), models are highly prone to hallucination. Overall, ACE shows a substantial gap between the performance of even the best models and consumers' AI needs.
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                DaLA：基于真实世界错误的丹麦语语言可接受性评估
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            DaLA: Danish Linguistic Acceptability Evaluation Guided by Real World Errors
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gianluca Barmina, Nathalie Carmen Hau Norman, Peter Schneider-Kamp, Lukas Galke
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定语言（丹麦语）的语言可接受性评估，属于纯粹的NLP评估基准研究。这与我的关注点（推荐系统、搜索、广告中的核心进展、LLM技术应用、Transformer架构改进等）完全无关，也没有展示出在推荐/搜索/广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 13:50:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04799v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04799v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present an enhanced benchmark for evaluating linguistic acceptability in Danish. We first analyze the most common errors found in written Danish. Based on this analysis, we introduce a set of fourteen corruption functions that generate incorrect sentences by systematically introducing errors into existing correct Danish sentences. To ensure the accuracy of these corruptions, we assess their validity using both manual and automatic methods. The results are then used as a benchmark for evaluating Large Language Models on a linguistic acceptability judgement task. Our findings demonstrate that this extension is both broader and more comprehensive than the current state of the art. By incorporating a greater variety of corruption types, our benchmark provides a more rigorous assessment of linguistic acceptability, increasing task difficulty, as evidenced by the lower performance of LLMs on our benchmark compared to existing ones. Our results also suggest that our benchmark has a higher discriminatory power which allows to better distinguish well-performing models from low-performing ones.
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            <a href="https://www.alphaxiv.org/abs/2512.04765v1" target="_blank" rel="noopener noreferrer">
                AdiBhashaa：一个社区策划的面向印度部落语言机器翻译的基准
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            AdiBhashaa: A Community-Curated Benchmark for Machine Translation into Indian Tribal Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pooja Singh, Sandeep Kumar
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于机器翻译基准构建，特别是针对印度部落语言，属于纯NLP领域。它不涉及推荐系统、搜索或广告的核心进展，也不涉及可能应用于这些领域的LLM/Transformer技术。标题中未暗示任何与异构数据统一建模（如VLM类比）或直接LLM应用相关的方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 13:01:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04765v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04765v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models and multilingual machine translation (MT) systems increasingly drive access to information, yet many languages of the tribal communities remain effectively invisible in these technologies. This invisibility exacerbates existing structural inequities in education, governance, and digital participation. We present AdiBhashaa, a community-driven initiative that constructs the first open parallel corpora and baseline MT systems for four major Indian tribal languages-Bhili, Mundari, Gondi, and Santali. This work combines participatory data creation with native speakers, human-in-the-loop validation, and systematic evaluation of both encoder-decoder MT models and large language models. In addition to reporting technical findings, we articulate how AdiBhashaa illustrates a possible model for more equitable AI research: it centers local expertise, builds capacity among early-career researchers from marginalized communities, and foregrounds human validation in the development of language technologies.
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            <a href="https://www.alphaxiv.org/abs/2512.04759v1" target="_blank" rel="noopener noreferrer">
                挑战大型语言模型在意大利语中的能力：一项社区倡议
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            Challenging the Abilities of Large Language Models in Italian: a Community Initiative
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Malvina Nissim, Danilo Croce, Viviana Patti, Pierpaolo Basile, Giuseppe Attanasi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于特定语言（意大利语）的LLM能力评估，属于纯粹的NLP评估基准研究，与您关注的推荐系统、搜索、广告领域的核心进展、LLM技术应用或Transformer架构改进无关。它不涉及任何跨模态建模、推荐算法改进或LLM在商业场景中的实际应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:50:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04759v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04759v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The rapid progress of Large Language Models (LLMs) has transformed natural language processing and broadened its impact across research and society. Yet, systematic evaluation of these models, especially for languages beyond English, remains limited. "Challenging the Abilities of LAnguage Models in ITAlian" (CALAMITA) is a large-scale collaborative benchmarking initiative for Italian, coordinated under the Italian Association for Computational Linguistics. Unlike existing efforts that focus on leaderboards, CALAMITA foregrounds methodology: it federates more than 80 contributors from academia, industry, and the public sector to design, document, and evaluate a diverse collection of tasks, covering linguistic competence, commonsense reasoning, factual consistency, fairness, summarization, translation, and code generation. Through this process, we not only assembled a benchmark of over 20 tasks and almost 100 subtasks, but also established a centralized evaluation pipeline that supports heterogeneous datasets and metrics. We report results for four open-weight LLMs, highlighting systematic strengths and weaknesses across abilities, as well as challenges in task-specific evaluation. Beyond quantitative results, CALAMITA exposes methodological lessons: the necessity of fine-grained, task-representative metrics, the importance of harmonized pipelines, and the benefits and limitations of broad community engagement. CALAMITA is conceived as a rolling benchmark, enabling continuous integration of new tasks and models. This makes it both a resource -- the most comprehensive and diverse benchmark for Italian to date -- and a framework for sustainable, community-driven evaluation. We argue that this combination offers a blueprint for other languages and communities seeking inclusive and rigorous LLM evaluation practices.
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            <a href="https://www.alphaxiv.org/abs/2512.04691v1" target="_blank" rel="noopener noreferrer">
                迈向大型语言模型多智能体系统的伦理：基于机制可解释性的视角
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            Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jae Hee Lee, Anne Lauscher, Stefano V. Albrecht
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于伦理和多智能体系统的机制可解释性，属于伦理/非技术性范畴，与用户关注的RecSys/Search/Ads核心技术进展、LLM/Transformer架构优化或直接应用完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:41:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04691v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04691v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.MA</span></div>
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                    Large language models (LLMs) have been widely deployed in various applications, often functioning as autonomous agents that interact with each other in multi-agent systems. While these systems have shown promise in enhancing capabilities and enabling complex tasks, they also pose significant ethical challenges. This position paper outlines a research agenda aimed at ensuring the ethical behavior of multi-agent systems of LLMs (MALMs) from the perspective of mechanistic interpretability. We identify three key research challenges: (i) developing comprehensive evaluation frameworks to assess ethical behavior at individual, interactional, and systemic levels; (ii) elucidating the internal mechanisms that give rise to emergent behaviors through mechanistic interpretability; and (iii) implementing targeted parameter-efficient alignment techniques to steer MALMs towards ethical behaviors without compromising their performance.
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            <a href="https://www.alphaxiv.org/abs/2512.04683v1" target="_blank" rel="noopener noreferrer">
                跨性别男性语言使用中的阳性形式：基于语料库的词位特异性差异研究
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            Geschlechtsübergreifende Maskulina im Sprachgebrauch Eine korpusbasierte Untersuchung zu lexemspezifischen Unterschieden
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Carolin Mueller-Spitzer, Samira Ochs, Jan Oliver Ruediger, Sascha Wolfer
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明这是一项语言学领域的语料库研究，专注于德语中跨性别男性的语言使用和词位差异。这与您关注的大型语言模型、推荐系统、搜索广告等技术领域完全无关，也不涉及Transformer架构、多模态建模或任何可能应用于推荐/搜索/广告系统的技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:27:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04683v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04683v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This study examines the distribution and linguistic characteristics of generic masculines (GM) in contemporary German press texts. The use of masculine personal nouns to refer to mixed-gender groups or unspecified individuals has been widely debated in academia and the public, with con-flicting perspectives on its gender-neutrality. While psycholinguistic studies suggest that GM is more readily associated with male referents, corpus-based analyses of its actual use remain scarce. We investigate GM in a large corpus of press texts, focusing on lexeme-specific differences across dif-ferent types of personal nouns. We conducted manual annotations of the whole inflectional para-digm of 21 personal nouns, resulting in 6,195 annotated tokens. Our findings reveal considerable differences between lexical items, especially between passive role nouns and prestige-related per-sonal nouns. On a grammatical level, we find that GM occurs predominantly in the plural and in indefinite noun phrases. Furthermore, our data shows that GM is not primarily used to denote entire classes of people, as has been previously claimed. By providing an empirical insight into the use of GM in authentic written language, we contribute to a more nuanced understanding of its forms and manifestations. These findings provide a solid basis for aligning linguistic stimuli in psy-cholinguistic studies more closely with real-world language use.
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            <a href="https://www.alphaxiv.org/abs/2512.04668v1" target="_blank" rel="noopener noreferrer">
                拓扑结构至关重要：在多智能体大语言模型中测量记忆泄漏
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            Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinbo Liu, Defu Cao, Yifei Wei, Tianyao Su, Yuan Liang, Yushun Dong, Yue Zhao, X...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于多智能体LLM中的记忆泄漏测量，这属于系统层面的性能评估问题，而非核心推荐/搜索/广告领域的算法或架构创新。虽然涉及LLM技术，但未提及任何与推荐系统、搜索或广告相关的潜在应用场景，也不属于Transformer架构改进、VLM类比或直接LLM应用范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:00:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04668v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04668v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a framework that measures how network structure shapes leakage. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions. We execute a two-phase protocol: Engram (seeding private information into a target agent's memory) and Resonance (multi-round interaction where an attacker attempts extraction). Over up to 10 interaction rounds, we quantify leakage as the fraction of ground-truth PII recovered from attacking agent outputs via exact matching. We systematically evaluate six common network topologies (fully connected, ring, chain, binary tree, star, and star-ring), varying agent counts $n\in\{4,5,6\}$, attacker-target placements, and base models. Our findings reveal consistent patterns: fully connected graphs exhibit maximum leakage while chains provide strongest protection; shorter attacker-target graph distance and higher target centrality significantly increase vulnerability; leakage rises sharply in early rounds before plateauing; model choice shifts absolute leakage rates but preserves topology rankings; temporal/locational PII attributes leak more readily than identity credentials or regulated identifiers. These results provide the first systematic mapping from architectural choices to measurable privacy risk, yielding actionable guidance: prefer sparse or hierarchical connectivity, maximize attacker-target separation, limit node degree and network radius, avoid shortcuts bypassing hubs, and implement topology-aware access controls.
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            <a href="https://www.alphaxiv.org/abs/2512.04642v1" target="_blank" rel="noopener noreferrer">
                语音极限环
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            Limit cycles for speech
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adamantios I. Gafos, Stephan R. Kuberski
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及语音处理，属于明确的无关主题（语音论文与推荐系统/搜索/广告无明确关联）。极限环是动力系统概念，可能涉及语音信号分析，但未表明与推荐系统、搜索或广告有任何技术联系。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 10:16:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04642v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04642v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">q-bio.NC</span><span class="category-tag">cs.CL</span></div>
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                    Rhythmic fluctuations in acoustic energy and accompanying neuronal excitations in cortical oscillations are characteristic of human speech, yet whether a corresponding rhythmicity inheres in the articulatory movements that generate speech remains unclear. The received understanding of speech movements as discrete, goal-oriented actions struggles to make contact with the rhythmicity findings. In this work, we demonstrate that an unintuitive -- but no less principled than the conventional -- representation for discrete movements reveals a pervasive limit cycle organization and unlocks the recovery of previously inaccessible rhythmic structure underlying the motor activity of speech. These results help resolve a time-honored tension between the ubiquity of biological rhythmicity and discreteness in speech, the quintessential human higher function, by revealing a rhythmic organization at the most fundamental level of individual articulatory actions.
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            <a href="https://www.alphaxiv.org/abs/2512.04578v1" target="_blank" rel="noopener noreferrer">
                LexGenius：面向法律通用智能的大型语言模型专家级基准
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            LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Sh...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于法律领域的LLM基准测试，属于特定领域应用而非核心推荐/搜索/广告技术。虽然涉及LLM评估，但属于纯粹NLP中心的基准测试话题，与用户行为建模、排序算法、多模态推荐等当前关注点无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:48:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04578v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04578v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making, simulating the expertise of legal experts across domains. However, existing benchmarks are result-oriented and fail to systematically evaluate the legal intelligence of large language models (LLMs), hindering the development of legal GI. To address this, we propose LexGenius, an expert-level Chinese legal benchmark for evaluating legal GI in LLMs. It follows a Dimension-Task-Ability framework, covering seven dimensions, eleven tasks, and twenty abilities. We use the recent legal cases and exam questions to create multiple-choice questions with a combination of manual and LLM reviews to reduce data leakage risks, ensuring accuracy and reliability through multiple rounds of checks. We evaluate 12 state-of-the-art LLMs using LexGenius and conduct an in-depth analysis. We find significant disparities across legal intelligence abilities for LLMs, with even the best LLMs lagging behind human legal professionals. We believe LexGenius can assess the legal intelligence abilities of LLMs and enhance legal GI development. Our project is available at https://github.com/QwenQKing/LexGenius.
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            <a href="https://www.alphaxiv.org/abs/2512.04518v1" target="_blank" rel="noopener noreferrer">
                UW-BioNLP在ChemoTimelines 2025任务中的工作：基于思考、微调与词典增强的大型语言模型系统用于化疗时间线提取
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            UW-BioNLP at ChemoTimelines 2025: Thinking, Fine-Tuning, and Dictionary-Enhanced LLM Systems for Chemotherapy Timeline Extraction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianmai M. Zhang, Zhaoyi Sun, Sihang Zeng, Chenxi Li, Neil F. Abernethy, Barbara...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学领域（化疗时间线提取）的生物医学自然语言处理应用，属于明确的医疗/生物学特定领域应用。根据用户指定的无关主题规则，医学/生物学领域应用属于应排除的范畴，且论文未显示与推荐系统、搜索或广告领域的任何潜在关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 06:59:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04518v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04518v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    The ChemoTimelines shared task benchmarks methods for constructing timelines of systemic anticancer treatment from electronic health records of cancer patients. This paper describes our methods, results, and findings for subtask 2 -- generating patient chemotherapy timelines from raw clinical notes. We evaluated strategies involving chain-of-thought thinking, supervised fine-tuning, direct preference optimization, and dictionary-based lookup to improve timeline extraction. All of our approaches followed a two-step workflow, wherein an LLM first extracted chemotherapy events from individual clinical notes, and then an algorithm normalized and aggregated events into patient-level timelines. Each specific method differed in how the associated LLM was utilized and trained. Multiple approaches yielded competitive performances on the test set leaderboard, with fine-tuned Qwen3-14B achieving the best official score of 0.678. Our results and analyses could provide useful insights for future attempts on this task as well as the design of similar tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.04396v1" target="_blank" rel="noopener noreferrer">
                基于经典机器学习和特征工程的Reddit平台讽刺检测
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            Sarcasm Detection on Reddit Using Classical Machine Learning and Feature Engineering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Subrata Karmaker
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于讽刺检测这一特定NLP任务，属于纯粹的文本分类问题，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及社交媒体数据，但论文方法基于经典机器学习而非Transformer架构，且没有展示在RecSys/Search/Ads领域的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 02:41:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04396v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04396v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Sarcasm is common in online discussions, yet difficult for machines to identify because the intended meaning often contradicts the literal wording. In this work, I study sarcasm detection using only classical machine learning methods and explicit feature engineering, without relying on neural networks or context from parent comments. Using a 100,000-comment subsample of the Self-Annotated Reddit Corpus (SARC 2.0), I combine word-level and character-level TF-IDF features with simple stylistic indicators. Four models are evaluated: logistic regression, a linear SVM, multinomial Naive Bayes, and a random forest. Naive Bayes and logistic regression perform the strongest, achieving F1-scores around 0.57 for sarcastic comments. Although the lack of conversational context limits performance, the results offer a clear and reproducible baseline for sarcasm detection using lightweight and interpretable methods.
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            <a href="https://www.alphaxiv.org/abs/2512.04374v1" target="_blank" rel="noopener noreferrer">
                LangSAT：一种结合自然语言处理与强化学习用于SAT求解的新型框架
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            LangSAT: A Novel Framework Combining NLP and Reinforcement Learning for SAT Solving
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muyu Pan, Matthew Walter, Dheeraj Kodakandla, Mahfuza Farooque
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及SAT求解（布尔可满足性问题），这是一个计算复杂性理论中的经典问题，与推荐系统、搜索或广告领域没有直接关联。虽然提到了NLP和强化学习，但应用场景是SAT求解而非推荐/搜索/广告任务，因此属于不相关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 01:47:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04374v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04374v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.FL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Our work presents a novel reinforcement learning (RL) based framework to optimize heuristic selection within the conflict-driven clause learning (CDCL) process, improving the efficiency of Boolean satisfia- bility (SAT) solving. The proposed system, LangSAT, bridges the gap between natural language inputs and propositional logic by converting English descriptions into Conjunctive Normal Form (CNF) expressions and solving them using an RL-enhanced CDCL SAT solver. Unlike existing SAT-solving platforms that require CNF as input, LangSAT enables users to input standard English descriptions, making SAT-solving more accessible. The framework comprises two key components: Lang2Logic, which translates English sentences into CNF expressions, and SmartSAT, an RL-based SAT solver. SmartSAT encodes clause-variable relationships as structured graph representations and extracts global features specific to the SAT problem. This implementation provides the RL agent with deeper contextual information, enabling SAT problems to be solved more efficiently. Lang2Logic was evaluated on diverse natural language inputs, processing descriptions up to 450 words. The generated CNFs were solved by SmartSAT, which demonstrated comparable performance to traditional CDCL heuristics with respect to solving time. The combined LangSAT framework offers a more accessible and scalable solution for SAT-solving tasks across reasoning, formal verification, and debugging.
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            <a href="https://www.alphaxiv.org/abs/2512.05117v1" target="_blank" rel="noopener noreferrer">
                通用权重子空间假说
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            The Universal Weight Subspace Hypothesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Prakhar Kaushik, Shravan Chaudhari, Ankit Vaidya, Rama Chellappa, Alan Yuille
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题描述了一个可能关于神经网络权重空间的通用理论假说，属于基础机器学习理论范畴。没有明确的技术细节表明与推荐系统、搜索、广告的直接关联，也没有提及Transformer架构、LLM技术或异构数据处理方法。标题过于宽泛，无法推断出在指定领域的实际应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05117v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05117v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We show that deep neural networks trained across diverse tasks exhibit remarkably similar low-dimensional parametric subspaces. We provide the first large-scale empirical evidence that demonstrates that neural networks systematically converge to shared spectral subspaces regardless of initialization, task, or domain. Through mode-wise spectral analysis of over 1100 models - including 500 Mistral-7B LoRAs, 500 Vision Transformers, and 50 LLaMA-8B models - we identify universal subspaces capturing majority variance in just a few principal directions. By applying spectral decomposition techniques to the weight matrices of various architectures trained on a wide range of tasks and datasets, we identify sparse, joint subspaces that are consistently exploited, within shared architectures across diverse tasks and datasets. Our findings offer new insights into the intrinsic organization of information within deep networks and raise important questions about the possibility of discovering these universal subspaces without the need for extensive data and computational resources. Furthermore, this inherent structure has significant implications for model reusability, multi-task learning, model merging, and the development of training and inference-efficient algorithms, potentially reducing the carbon footprint of large-scale neural models.
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            <a href="https://www.alphaxiv.org/abs/2512.05115v1" target="_blank" rel="noopener noreferrer">
                Light-X：具有相机与光照控制的生成式4D视频渲染
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            Light-X: Generative 4D Video Rendering with Camera and Illumination Control
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianqi Liu, Zhaoxi Chen, Zihao Huang, Shaocong Xu, Saining Zhang, Chongjie Ye, B...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机图形学中的4D视频渲染技术，涉及相机和光照控制，属于纯粹的视觉/图形学领域。虽然标题包含“生成式”一词，但内容与推荐系统、搜索或广告的核心技术（如排序、检索、用户建模）无直接关联，也未提及任何可能应用于这些领域的Transformer架构或LLM技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05115v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05115v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in illumination control extend image-based methods to video, yet still facing a trade-off between lighting fidelity and temporal consistency. Moving beyond relighting, a key step toward generative modeling of real-world scenes is the joint control of camera trajectory and illumination, since visual dynamics are inherently shaped by both geometry and lighting. To this end, we present Light-X, a video generation framework that enables controllable rendering from monocular videos with both viewpoint and illumination control. 1) We propose a disentangled design that decouples geometry and lighting signals: geometry and motion are captured via dynamic point clouds projected along user-defined camera trajectories, while illumination cues are provided by a relit frame consistently projected into the same geometry. These explicit, fine-grained cues enable effective disentanglement and guide high-quality illumination. 2) To address the lack of paired multi-view and multi-illumination videos, we introduce Light-Syn, a degradation-based pipeline with inverse-mapping that synthesizes training pairs from in-the-wild monocular footage. This strategy yields a dataset covering static, dynamic, and AI-generated scenes, ensuring robust training. Extensive experiments show that Light-X outperforms baseline methods in joint camera-illumination control and surpasses prior video relighting methods under both text- and background-conditioned settings.
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            <a href="https://www.alphaxiv.org/abs/2512.05114v1" target="_blank" rel="noopener noreferrer">
                基于多对比度MRI的婴儿大脑深度分割
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            Deep infant brain segmentation from multi-contrast MRI
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Malte Hoffmann, Lilla Zöllei, Adrian V. Dalca
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学影像分析（婴儿大脑MRI分割），属于明确的医学领域应用。这完全属于用户指定的无关主题中的“Medical, Biology, Chemistry, Physics or other domain-specific applications”，与推荐系统、搜索、广告或相关使能技术没有任何直接或间接的关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05114v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05114v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span><span class="category-tag">eess.IV</span></div>
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                    Segmentation of magnetic resonance images (MRI) facilitates analysis of human brain development by delineating anatomical structures. However, in infants and young children, accurate segmentation is challenging due to development and imaging constraints. Pediatric brain MRI is notoriously difficult to acquire, with inconsistent availability of imaging modalities, substantial non-head anatomy in the field of view, and frequent motion artifacts. This has led to specialized segmentation models that are often limited to specific image types or narrow age groups, or that are fragile for more variable images such as those acquired clinically. We address this method fragmentation with BabySeg, a deep learning brain segmentation framework for infants and young children that supports diverse MRI protocols, including repeat scans and image types unavailable during training. Our approach builds on recent domain randomization techniques, which synthesize training images far beyond realistic bounds to promote dataset shift invariance. We also describe a mechanism that enables models to flexibly pool and interact features from any number of input scans. We demonstrate state-of-the-art performance that matches or exceeds the accuracy of several existing methods for various age cohorts and input configurations using a single model, in a fraction of the runtime required by many existing tools.
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            <a href="https://www.alphaxiv.org/abs/2512.05113v1" target="_blank" rel="noopener noreferrer">
                Splannequin：通过双重检测溅射技术冻结单目人体模型挑战视频
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            Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao-Jen Chien, Yi-Chuan Huang, Chung-Ho Wu, Wei-Lun Chao, Yu-Lun Liu
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文标题明确涉及计算机视觉中的单目视频处理和人体模型技术，属于纯粹的视觉领域研究。标题中提到的'Monocular Mannequin-Challenge Footage'和'Dual-Detection Splatting'都是视觉处理技术，与推荐系统、搜索或广告的核心技术栈没有直接关联，也不符合任何指定的关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05113v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05113v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstruction. Instead of focusing on modeling motion, our goal is to create a frozen scene while strategically preserving subtle dynamics to enable user-controlled instant selection. To achieve this, we introduce a novel application of dynamic Gaussian splatting: the scene is modeled dynamically, which retains nearby temporal variation, and a static scene is rendered by fixing the model's time parameter. However, under this usage, monocular capture with sparse temporal supervision introduces artifacts like ghosting and blur for Gaussians that become unobserved or occluded at weakly supervised timestamps. We propose Splannequin, an architecture-agnostic regularization that detects two states of Gaussian primitives, hidden and defective, and applies temporal anchoring. Under predominantly forward camera motion, hidden states are anchored to their recent well-observed past states, while defective states are anchored to future states with stronger supervision. Our method integrates into existing dynamic Gaussian pipelines via simple loss terms, requires no architectural changes, and adds zero inference overhead. This results in markedly improved visual quality, enabling high-fidelity, user-selectable frozen-time renderings, validated by a 96% user preference. Project page: https://chien90190.github.io/splannequin/
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            <a href="https://www.alphaxiv.org/abs/2512.05110v1" target="_blank" rel="noopener noreferrer">
                ShadowDraw：从任意物体到阴影绘画的组合艺术
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            ShadowDraw: From Any Object to Shadow-Drawing Compositional Art
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rundong Luo, Noah Snavely, Wei-Chiu Ma
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的艺术生成技术，属于纯粹的视觉或图形学领域，与推荐系统、搜索或广告的核心技术没有直接关联。根据您的关注点，这属于'纯粹的视觉、3D视觉、图形或语音论文，与RecSys/Search/Ads没有明确相关性'的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05110v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05110v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.GR</span></div>
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                    We introduce ShadowDraw, a framework that transforms ordinary 3D objects into shadow-drawing compositional art. Given a 3D object, our system predicts scene parameters, including object pose and lighting, together with a partial line drawing, such that the cast shadow completes the drawing into a recognizable image. To this end, we optimize scene configurations to reveal meaningful shadows, employ shadow strokes to guide line drawing generation, and adopt automatic evaluation to enforce shadow-drawing coherence and visual quality. Experiments show that ShadowDraw produces compelling results across diverse inputs, from real-world scans and curated datasets to generative assets, and naturally extends to multi-object scenes, animations, and physical deployments. Our work provides a practical pipeline for creating shadow-drawing art and broadens the design space of computational visual art, bridging the gap between algorithmic design and artistic storytelling. Check out our project page https://red-fairy.github.io/ShadowDraw/ for more results and an end-to-end real-world demonstration of our pipeline!
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            <a href="https://www.alphaxiv.org/abs/2512.05106v1" target="_blank" rel="noopener noreferrer">
                NeuralRemaster：用于结构对齐生成的相位保持扩散模型
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            NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu Zeng, Charles Ochoa, Mingyuan Zhou, Vishal M. Patel, Vitor Guizilini, Rowan M...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及扩散模型和生成任务，属于纯粹的生成式AI内容生成领域，与AIGC相关。这完全属于您列出的无关主题中的'AIGC, Content generation, Summarization, or other purely LLM-centric topics'，与推荐系统、搜索或广告的核心进展、使能技术或直接应用没有明确关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05106v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05106v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Standard diffusion corrupts data using Gaussian noise whose Fourier coefficients have random magnitudes and random phases. While effective for unconditional or text-to-image generation, corrupting phase components destroys spatial structure, making it ill-suited for tasks requiring geometric consistency, such as re-rendering, simulation enhancement, and image-to-image translation. We introduce Phase-Preserving Diffusion φ-PD, a model-agnostic reformulation of the diffusion process that preserves input phase while randomizing magnitude, enabling structure-aligned generation without architectural changes or additional parameters. We further propose Frequency-Selective Structured (FSS) noise, which provides continuous control over structural rigidity via a single frequency-cutoff parameter. φ-PD adds no inference-time cost and is compatible with any diffusion model for images or videos. Across photorealistic and stylized re-rendering, as well as sim-to-real enhancement for driving planners, φ-PD produces controllable, spatially aligned results. When applied to the CARLA simulator, φ-PD improves CARLA-to-Waymo planner performance by 50\%. The method is complementary to existing conditioning approaches and broadly applicable to image-to-image and video-to-video generation. Videos, additional examples, and code are available on our \href{https://yuzeng-at-tri.github.io/ppd-page/}{project page}.
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            <a href="https://www.alphaxiv.org/abs/2512.05104v1" target="_blank" rel="noopener noreferrer">
                EvoIR：通过进化频率调制实现一体化图像修复
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            EvoIR: Towards All-in-One Image Restoration via Evolutionary Frequency Modulation
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiaqi Ma, Shengkai Hu, Jun Wan, Jiaxing Huang, Lefei Zhang, Salman Khan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像修复这一计算机视觉任务，属于纯粹的视觉处理范畴。虽然标题中提到“一体化”概念，但论文内容明显局限于图像质量增强技术，与推荐系统、搜索或广告中的排序、匹配、用户建模等核心问题没有直接关联，也不涉及LLM或Transformer架构在非视觉领域的应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05104v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05104v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    All-in-One Image Restoration (AiOIR) tasks often involve diverse degradation that require robust and versatile strategies. However, most existing approaches typically lack explicit frequency modeling and rely on fixed or heuristic optimization schedules, which limit the generalization across heterogeneous degradation. To address these limitations, we propose EvoIR, an AiOIR-specific framework that introduces evolutionary frequency modulation for dynamic and adaptive image restoration. Specifically, EvoIR employs the Frequency-Modulated Module (FMM) that decomposes features into high- and low-frequency branches in an explicit manner and adaptively modulates them to enhance both structural fidelity and fine-grained details. Central to EvoIR, an Evolutionary Optimization Strategy (EOS) iteratively adjusts frequency-aware objectives through a population-based evolutionary process, dynamically balancing structural accuracy and perceptual fidelity. Its evolutionary guidance further mitigates gradient conflicts across degradation and accelerates convergence. By synergizing FMM and EOS, EvoIR yields greater improvements than using either component alone, underscoring their complementary roles. Extensive experiments on multiple benchmarks demonstrate that EvoIR outperforms state-of-the-art AiOIR methods.
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            <a href="https://www.alphaxiv.org/abs/2512.05103v1" target="_blank" rel="noopener noreferrer">
                TV2TV：一种用于交错语言与视频生成的统一框架
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            TV2TV: A Unified Framework for Interleaved Language and Video Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaochuang Han, Youssef Emad, Melissa Hall, John Nguyen, Karthik Padthe, Liam Ro...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视频生成与语言交错，属于纯粹的视觉内容生成领域，与推荐系统、搜索或广告的核心排名任务无关。虽然涉及多模态（语言+视频），但缺乏将异构数据（如上下文特征、用户序列）作为不同模态进行统一建模的类比潜力，且不涉及Transformer架构效率、注意力机制等使能技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:59:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05103v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05103v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video generation models are rapidly advancing, but can still struggle with complex video outputs that require significant semantic branching or repeated high-level reasoning about what should happen next. In this paper, we introduce a new class of omni video-text models that integrate ideas from recent LM reasoning advances to address this challenge. More specifically, we present TV2TV, a unified generative modeling framework which decomposes video generation into an interleaved text and video generation process. TV2TV jointly learns language modeling (next-token prediction) and video flow matching (next-frame prediction) using a Mixture-of-Transformers (MoT) architecture. At inference time, TV2TV decides when to alternate between generating text and video frames, allowing the model to "think in words" about subsequent content before ``acting in pixels'' to produce frames. This design offloads much of the responsibility for deciding what should happen next to the language modeling tower, enabling improved visual quality and prompt alignment of generated videos. It also enables fine-grained controllability, allowing users to modify the video generation trajectory through text interventions at any point in the process. In controlled experiments on video game data, TV2TV demonstrates substantial improvements in both visual quality and controllability. TV2TV also scales to natural videos, as we show by augmenting sports videos with interleaved natural language action descriptions using vision-language models (VLMs). Training TV2TV on this corpus yields strong visual quality and prompt alignment, showcasing the model's ability to reason about and generate complex real-world action sequences. Together, these results highlight TV2TV as a promising step toward video generation with open-ended textual reasoning and control.
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            <a href="https://www.alphaxiv.org/abs/2512.05094v1" target="_blank" rel="noopener noreferrer">
                从生成的人类视频到物理可行的机器人轨迹
            </a>
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            From Generated Human Videos to Physically Plausible Robot Trajectories
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>James Ni, Zekai Wang, Wei Lin, Amir Bar, Yann LeCun, Trevor Darrell, Jitendra Ma...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及机器人轨迹生成和视频分析，属于机器人学和计算机视觉领域。虽然提到了生成模型，但核心关注点是物理可行性和机器人控制，与推荐系统、搜索、广告等当前关注领域没有直接关联。标题中没有任何元素表明该技术可应用于推荐、搜索或广告系统。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:56:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05094v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05094v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    Video generation models are rapidly improving in their ability to synthesize human actions in novel contexts, holding the potential to serve as high-level planners for contextual robot control. To realize this potential, a key research question remains open: how can a humanoid execute the human actions from generated videos in a zero-shot manner? This challenge arises because generated videos are often noisy and exhibit morphological distortions that make direct imitation difficult compared to real video. To address this, we introduce a two-stage pipeline. First, we lift video pixels into a 4D human representation and then retarget to the humanoid morphology. Second, we propose GenMimic-a physics-aware reinforcement learning policy conditioned on 3D keypoints, and trained with symmetry regularization and keypoint-weighted tracking rewards. As a result, GenMimic can mimic human actions from noisy, generated videos. We curate GenMimicBench, a synthetic human-motion dataset generated using two video generation models across a spectrum of actions and contexts, establishing a benchmark for assessing zero-shot generalization and policy robustness. Extensive experiments demonstrate improvements over strong baselines in simulation and confirm coherent, physically stable motion tracking on a Unitree G1 humanoid robot without fine-tuning. This work offers a promising path to realizing the potential of video generation models as high-level policies for robot control.
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            <a href="https://www.alphaxiv.org/abs/2512.05091v1" target="_blank" rel="noopener noreferrer">
                视觉推理追踪器：面向对象级基础推理的基准测试
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Visual Reasoning Tracer: Object-Level Grounded Reasoning Benchmark
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haobo Yuan, Yueyi Sun, Yanwei Li, Tao Zhang, Xueqing Deng, Henghui Ding, Lu Qi, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向计算机视觉领域的基准测试，专注于视觉推理和对象级基础任务。虽然提到了“推理”，但这属于纯粹的视觉推理基准，与推荐系统、搜索或广告中的文本/序列推理无关，也没有涉及LLM技术或异构数据建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:55:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05091v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05091v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved performance on tasks such as visual grounding and visual question answering. However, the reasoning processes of these models remain largely opaque; they typically output only final predictions without revealing the intermediate steps or fine-grained evidence (e.g., pixels, locations) that lead to the result. This contrasts with human intelligence, which naturally operates through a chain of visual reasoning. To address this limitation, we introduce the Visual Reasoning Tracer (VRT) task, which requires models to not only localize the target object but also explicitly predict the intermediate objects that form the reasoning path. To advance research in this area, we contribute: (1) VRT-Bench, a human-annotated benchmark for evaluating visual reasoning; (2) a new metric for assessing the quality of reasoning traces; and (3) VRT-80k, a large-scale dataset for reasoning model training. Our experiments reveal that while existing models often produce the correct final output, they struggle to ground their intermediate reasoning. In contrast, models trained on VRT-80k achieve substantial improvements in tracing the reasoning path.
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            <a href="https://www.alphaxiv.org/abs/2512.05081v1" target="_blank" rel="noopener noreferrer">
                深度强制：基于深度汇与参与式压缩的无训练长视频生成
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            Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jung Yi, Wooseok Jang, Paul Hyunbin Cho, Jisu Nam, Heeji Yoon, Seungryong Kim
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频生成技术，属于纯粹的视觉内容生成领域。虽然提到了“深度汇”和“参与式压缩”等技术概念，但论文的核心是长视频生成，这与推荐系统、搜索或广告中的排名、检索或个性化任务没有直接关联。它属于AIGC/内容生成类别，被明确列为不相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:46:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05081v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05081v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in autoregressive video diffusion have enabled real-time frame streaming, yet existing solutions still suffer from temporal repetition, drift, and motion deceleration. We find that naively applying StreamingLLM-style attention sinks to video diffusion leads to fidelity degradation and motion stagnation. To overcome this, we introduce Deep Forcing, which consists of two training-free mechanisms that address this without any fine-tuning. Specifically, 1) Deep Sink dedicates half of the sliding window to persistent sink tokens and re-aligns their temporal RoPE phase to the current timeline, stabilizing global context during long rollouts. 2) Participative Compression performs importance-aware KV cache pruning that preserves only tokens actively participating in recent attention while safely discarding redundant and degraded history, minimizing error accumulation under out-of-distribution length generation. Together, these components enable over 12x extrapolation (e.g. 5s-trained to 60s+ generation) with better imaging quality than LongLive, better aesthetic quality than RollingForcing, almost maintaining overall consistency, and substantial gains in dynamic degree, all while maintaining real-time generation. Our results demonstrate that training-free KV-cache management can match or exceed training-based approaches for autoregressively streaming long-video generation.
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            <a href="https://www.alphaxiv.org/abs/2512.05079v1" target="_blank" rel="noopener noreferrer">
                基于生成先验与接触诱导约束的遮挡条件下物体重建
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        <div class="mb-2 text-base text-gray-700">
            Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minghan Zhu, Zhiyi Wang, Qihang Sun, Maani Ghaffari, Michael Posa
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于计算机视觉中的物体重建问题，特别是处理遮挡场景，涉及生成模型先验和物理接触约束。这属于纯粹的3D视觉/图形学研究，没有展示与推荐系统、搜索或广告领域的明显关联。标题中提到的技术（生成先验、接触约束）在推荐/搜索/广告的典型任务（如排序、召回、用户建模）中没有直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:45:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05079v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05079v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Object geometry is key information for robot manipulation. Yet, object reconstruction is a challenging task because cameras only capture partial observations of objects, especially when occlusion occurs. In this paper, we leverage two extra sources of information to reduce the ambiguity of vision signals. First, generative models learn priors of the shapes of commonly seen objects, allowing us to make reasonable guesses of the unseen part of geometry. Second, contact information, which can be obtained from videos and physical interactions, provides sparse constraints on the boundary of the geometry. We combine the two sources of information through contact-guided 3D generation. The guidance formulation is inspired by drag-based editing in generative models. Experiments on synthetic and real-world data show that our approach improves the reconstruction compared to pure 3D generation and contact-based optimization.
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            <a href="https://www.alphaxiv.org/abs/2512.05076v1" target="_blank" rel="noopener noreferrer">
                BulletTime：视频生成中时间与相机位姿的解耦控制
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            BulletTime: Decoupled Control of Time and Camera Pose for Video Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiming Wang, Qihang Zhang, Shengqu Cai, Tong Wu, Jan Ackermann, Zhengfei Kuang, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于视频生成中的时间与相机位姿控制技术，属于计算机视觉和视频生成领域。虽然涉及生成模型，但未明确提及推荐系统、搜索或广告应用，也未涉及LLM、Transformer架构或异构数据处理等当前关注的核心技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 18:40:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05076v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05076v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to provide precise spatial and temporal control. We introduce a 4D-controllable video diffusion framework that explicitly decouples scene dynamics from camera pose, enabling fine-grained manipulation of both scene dynamics and camera viewpoint. Our framework takes continuous world-time sequences and camera trajectories as conditioning inputs, injecting them into the video diffusion model through a 4D positional encoding in the attention layer and adaptive normalizations for feature modulation. To train this model, we curate a unique dataset in which temporal and camera variations are independently parameterized; this dataset will be made public. Experiments show that our model achieves robust real-world 4D control across diverse timing patterns and camera trajectories, while preserving high generation quality and outperforming prior work in controllability. See our website for video results: https://19reborn.github.io/Bullet4D/
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            <a href="https://www.alphaxiv.org/abs/2512.05044v1" target="_blank" rel="noopener noreferrer">
                基于单张图像的联合三维几何重建与运动生成用于四维合成
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            Joint 3D Geometry Reconstruction and Motion Generation for 4D Synthesis from a Single Image
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanran Zhang, Ziyi Wang, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及3D几何重建、运动生成和4D合成，这些都属于计算机视觉和图形学领域，与您的关注点（推荐系统、搜索、广告、LLM/Transformer技术及其应用）没有直接关联。标题中提到的技术没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:59:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05044v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05044v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and reconstruct-then-generate methods decouple geometry from motion, causing spatiotemporal inconsistencies and poor generalization. To address these, we extend the reconstruct-then-generate framework to jointly perform Motion generation and geometric Reconstruction for 4D Synthesis (MoRe4D). We first introduce TrajScene-60K, a large-scale dataset of 60,000 video samples with dense point trajectories, addressing the scarcity of high-quality 4D scene data. Based on this, we propose a diffusion-based 4D Scene Trajectory Generator (4D-STraG) to jointly generate geometrically consistent and motion-plausible 4D point trajectories. To leverage single-view priors, we design a depth-guided motion normalization strategy and a motion-aware module for effective geometry and dynamics integration. We then propose a 4D View Synthesis Module (4D-ViSM) to render videos with arbitrary camera trajectories from 4D point track representations. Experiments show that MoRe4D generates high-quality 4D scenes with multi-view consistency and rich dynamic details from a single image. Code: https://github.com/Zhangyr2022/MoRe4D.
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            <a href="https://www.alphaxiv.org/abs/2512.05039v1" target="_blank" rel="noopener noreferrer">
                基于语义引导与混合感知编码的两阶段生成对抗网络人脸修复方法
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Semantic-Guided Two-Stage GAN for Face Inpainting with Hybrid Perceptual Encoding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abhigyan Bhattacharya, Hiranmoy Roy
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人脸修复任务，属于纯粹的图像生成技术。虽然涉及GAN和感知编码，但其核心应用场景（人脸修复）与推荐系统、搜索或广告的排序任务没有直接关联。论文没有展示在异构数据处理、多模态建模或推荐/搜索应用方面的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:56:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05039v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05039v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Facial Image inpainting aim is to restore the missing or corrupted regions in face images while preserving identity, structural consistency and photorealistic image quality, a task specifically created for photo restoration. Though there are recent lot of advances in deep generative models, existing methods face problems with large irregular masks, often producing blurry textures on the edges of the masked region, semantic inconsistencies, or unconvincing facial structures due to direct pixel level synthesis approach and limited exploitation of facial priors. In this paper we propose a novel architecture, which address these above challenges through semantic-guided hierarchical synthesis. Our approach starts with a method that organizes and synthesizes information based on meaning, followed by refining the texture. This process gives clear insights into the facial structure before we move on to creating detailed images. In the first stage, we blend two techniques: one that focuses on local features with CNNs and global features with Vision Transformers. This helped us create clear and detailed semantic layouts. In the second stage, we use a Multi-Modal Texture Generator to refine these layouts by pulling in information from different scales, ensuring everything looks cohesive and consistent. The architecture naturally handles arbitrary mask configurations through dynamic attention without maskspecific training. Experiment on two datasets CelebA-HQ and FFHQ shows that our model outperforms other state-of-the-art methods, showing improvements in metrics like LPIPS, PSNR, and SSIM. It produces visually striking results with better semantic preservation, in challenging large-area inpainting situations.
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            <a href="https://www.alphaxiv.org/abs/2512.05025v1" target="_blank" rel="noopener noreferrer">
                RAMEN：适用于地球观测的分辨率可调多模态编码器
            </a>
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            RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nicolas Houdré, Diego Marcos, Hugo Riffaud de Turckheim, Dino Ienco, Laurent Wen...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向地球观测这一特定领域应用，属于明确的无关主题（Medical, Biology, Chemistry, Physics or other domain-specific applications）。虽然涉及多模态编码器技术，但核心应用领域与推荐系统、搜索或广告完全无关，且未表明任何潜在的跨领域应用可能性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:40:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05025v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05025v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Earth observation (EO) data spans a wide range of spatial, spectral, and temporal resolutions, from high-resolution optical imagery to low resolution multispectral products or radar time series. While recent foundation models have improved multimodal integration for learning meaningful representations, they often expect fixed input resolutions or are based on sensor-specific encoders limiting generalization across heterogeneous EO modalities. To overcome these limitations we introduce RAMEN, a resolution-adjustable multimodal encoder that learns a shared visual representation across EO data in a fully sensor-agnostic manner. RAMEN treats the modality and spatial and temporal resolutions as key input data features, enabling coherent analysis across modalities within a unified latent space. Its main methodological contribution is to define spatial resolution as a controllable output parameter, giving users direct control over the desired level of detail at inference and allowing explicit trade-offs between spatial precision and computational cost. We train a single, unified transformer encoder reconstructing masked multimodal EO data drawn from diverse sources, ensuring generalization across sensors and resolutions. Once pretrained, RAMEN transfers effectively to both known and unseen sensor configurations and outperforms larger state-of-the-art models on the community-standard PANGAEA benchmark, containing various multi-sensor and multi-resolution downstream tasks. Our code and pretrained model are available at https://github.com/nicolashoudre/RAMEN.
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            <a href="https://www.alphaxiv.org/abs/2512.05021v1" target="_blank" rel="noopener noreferrer">
                HTR-ConvText：利用卷积和文本信息进行手写文本识别
            </a>
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            HTR-ConvText: Leveraging Convolution and Textual Information for Handwritten Text Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pham Thach Thanh Truc, Dang Hoai Nam, Huynh Tong Dang Khoa, Vo Nguyen Le Duy
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于手写文本识别这一计算机视觉任务，属于纯粹的视觉处理领域。虽然涉及文本信息，但核心是识别手写字符而非语言理解，与推荐系统、搜索或广告中的异构数据统一建模、LLM应用或Transformer架构进展没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:35:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05021v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05021v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Handwritten Text Recognition remains challenging due to the limited data, high writing style variance, and scripts with complex diacritics. Existing approaches, though partially address these issues, often struggle to generalize without massive synthetic data. To address these challenges, we propose HTR-ConvText, a model designed to capture fine-grained, stroke-level local features while preserving global contextual dependencies. In the feature extraction stage, we integrate a residual Convolutional Neural Network backbone with a MobileViT with Positional Encoding block. This enables the model to both capture structural patterns and learn subtle writing details. We then introduce the ConvText encoder, a hybrid architecture combining global context and local features within a hierarchical structure that reduces sequence length for improved efficiency. Additionally, an auxiliary module injects textual context to mitigate the weakness of Connectionist Temporal Classification. Evaluations on IAM, READ2016, LAM and HANDS-VNOnDB demonstrate that our approach achieves improved performance and better generalization compared to existing methods, especially in scenarios with limited training samples and high handwriting diversity.
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            <a href="https://www.alphaxiv.org/abs/2512.05016v1" target="_blank" rel="noopener noreferrer">
                基于视频扩散先验的生成式神经视频压缩
            </a>
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            Generative Neural Video Compression via Video Diffusion Prior
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qi Mao, Hao Cheng, Tinghan Yang, Libiao Jin, Siwei Ma
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频压缩技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术栈无直接关联。虽然标题包含'扩散'这一与生成模型相关的术语，但论文内容明显属于AIGC/内容生成范畴，已被明确列为无关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:27:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05016v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05016v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present GNVC-VD, the first DiT-based generative neural video compression framework built upon an advanced video generation foundation model, where spatio-temporal latent compression and sequence-level generative refinement are unified within a single codec. Existing perceptual codecs primarily rely on pre-trained image generative priors to restore high-frequency details, but their frame-wise nature lacks temporal modeling and inevitably leads to perceptual flickering. To address this, GNVC-VD introduces a unified flow-matching latent refinement module that leverages a video diffusion transformer to jointly enhance intra- and inter-frame latents through sequence-level denoising, ensuring consistent spatio-temporal details. Instead of denoising from pure Gaussian noise as in video generation, GNVC-VD initializes refinement from decoded spatio-temporal latents and learns a correction term that adapts the diffusion prior to compression-induced degradation. A conditioning adaptor further injects compression-aware cues into intermediate DiT layers, enabling effective artifact removal while maintaining temporal coherence under extreme bitrate constraints. Extensive experiments show that GNVC-VD surpasses both traditional and learned codecs in perceptual quality and significantly reduces the flickering artifacts that persist in prior generative approaches, even below 0.01 bpp, highlighting the promise of integrating video-native generative priors into neural codecs for next-generation perceptual video compression.
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            <a href="https://www.alphaxiv.org/abs/2512.05006v1" target="_blank" rel="noopener noreferrer">
                基于非透明物体深度信息的自监督透明物体深度补全学习
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        <div class="mb-2 text-base text-gray-700">
            Self-Supervised Learning for Transparent Object Depth Completion Using Depth from Non-Transparent Objects
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xianghui Fan, Zhaoyu Chen, Mengyang Pan, Anping Deng, Hang Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的深度补全技术，特别是针对透明物体的处理，这属于纯粹的视觉领域研究。虽然涉及自监督学习，但论文内容与推荐系统、搜索或广告的核心技术没有直接关联，也没有展示出在异构数据处理或Transformer架构方面的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:17:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05006v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05006v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The perception of transparent objects is one of the well-known challenges in computer vision. Conventional depth sensors have difficulty in sensing the depth of transparent objects due to refraction and reflection of light. Previous research has typically train a neural network to complete the depth acquired by the sensor, and this method can quickly and accurately acquire accurate depth maps of transparent objects. However, previous training relies on a large amount of annotation data for supervision, and the labeling of depth maps is costly. To tackle this challenge, we propose a new self-supervised method for training depth completion networks. Our method simulates the depth deficits of transparent objects within non-transparent regions and utilizes the original depth map as ground truth for supervision. Experiments demonstrate that our method achieves performance comparable to supervised approach, and pre-training with our method can improve the model performance when the training samples are small.
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                通过扩散变换器的高效适配实现反射去除
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            Reflection Removal through Efficient Adaptation of Diffusion Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniyar Zakarin, Thiemo Wandel, Anton Obukhov, Dengxin Dai
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于计算机视觉中的反射去除任务，属于纯粹的视觉处理问题。虽然提到了扩散变换器（Diffusion Transformers），但这属于特定视觉生成模型的应用，与推荐系统、搜索或广告中的异质数据处理、Transformer架构效率提升或LLM应用无关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:12:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.05000v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.05000v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce a diffusion-transformer (DiT) framework for single-image reflection removal that leverages the generalization strengths of foundation diffusion models in the restoration setting. Rather than relying on task-specific architectures, we repurpose a pre-trained DiT-based foundation model by conditioning it on reflection-contaminated inputs and guiding it toward clean transmission layers. We systematically analyze existing reflection removal data sources for diversity, scalability, and photorealism. To address the shortage of suitable data, we construct a physically based rendering (PBR) pipeline in Blender, built around the Principled BSDF, to synthesize realistic glass materials and reflection effects. Efficient LoRA-based adaptation of the foundation model, combined with the proposed synthetic data, achieves state-of-the-art performance on in-domain and zero-shot benchmarks. These results demonstrate that pretrained diffusion transformers, when paired with physically grounded data synthesis and efficient adaptation, offer a scalable and high-fidelity solution for reflection removal. Project page: https://hf.co/spaces/huawei-bayerlab/windowseat-reflection-removal-web
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04996v1" target="_blank" rel="noopener noreferrer">
                面向嵌入式GPU上基于膨胀的ICP算法的动态内存分配策略
            </a>
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        <div class="mb-2 text-base text-gray-700">
            A dynamic memory assignment strategy for dilation-based ICP algorithm on embedded GPUs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qiong Chang, Weimin Wang, Junpei Zhong, Jun Miyazaki
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及嵌入式GPU上的动态内存分配策略和ICP（迭代最近点）算法，这属于计算机视觉中的3D配准技术。虽然提到了GPU优化，但核心内容与推荐系统、搜索或广告的排名、检索、建模等关键技术无关，也没有涉及LLM、Transformer架构或异构数据处理。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 17:10:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04996v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04996v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper proposes a memory-efficient optimization strategy for the high-performance point cloud registration algorithm VANICP, enabling lightweight execution on embedded GPUs with constrained hardware resources. VANICP is a recently published acceleration framework that significantly improves the computational efficiency of point-cloud-based applications. By transforming the global nearest neighbor search into a localized process through a dilation-based information propagation mechanism, VANICP greatly reduces the computational complexity of the NNS. However, its original implementation demands a considerable amount of memory, which restricts its deployment in resource-constrained environments such as embedded systems. To address this issue, we propose a GPU-oriented dynamic memory assignment strategy that optimizes the memory usage of the dilation operation. Furthermore, based on this strategy, we construct an enhanced version of the VANICP framework that achieves over 97% reduction in memory consumption while preserving the original performance. Source code is published on: https://github.com/changqiong/VANICP4Em.git.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04981v1" target="_blank" rel="noopener noreferrer">
                对齐但刻板？系统提示对基于LVLM的文本到图像模型中社会偏见的隐性影响
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Aligned but Stereotypical? The Hidden Influence of System Prompts on Social Bias in LVLM-Based Text-to-Image Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>NaHyeon Park, Namin An, Kunhee Kim, Soyeon Yoon, Jiahao Huo, Hyunjung Shim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LVLM（大型视觉语言模型）中的社会偏见问题，属于伦理/公平性范畴，这在您的无关主题列表中明确排除。虽然涉及多模态模型，但核心关注点是偏见评估而非推荐/搜索/广告的技术应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:52:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04981v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04981v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large vision-language model (LVLM) based text-to-image (T2I) systems have become the dominant paradigm in image generation, yet whether they amplify social biases remains insufficiently understood. In this paper, we show that LVLM-based models produce markedly more socially biased images than non-LVLM-based models. We introduce a 1,024 prompt benchmark spanning four levels of linguistic complexity and evaluate demographic bias across multiple attributes in a systematic manner. Our analysis identifies system prompts, the predefined instructions guiding LVLMs, as a primary driver of biased behavior. Through decoded intermediate representations, token-probability diagnostics, and embedding-association analyses, we reveal how system prompts encode demographic priors that propagate into image synthesis. To this end, we propose FairPro, a training-free meta-prompting framework that enables LVLMs to self-audit and construct fairness-aware system prompts at test time. Experiments on two LVLM-based T2I models, SANA and Qwen-Image, show that FairPro substantially reduces demographic bias while preserving text-image alignment. We believe our findings provide deeper insight into the central role of system prompts in bias propagation and offer a practical, deployable approach for building more socially responsible T2I systems.
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            <a href="https://www.alphaxiv.org/abs/2512.04970v1" target="_blank" rel="noopener noreferrer">
                面向语义与几何任务的稳定单像素对比学习
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            Stable Single-Pixel Contrastive Learning for Semantic and Geometric Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leonid Pogorelyuk, Niels Bracher, Aaron Verkleeren, Lars Kühmichel, Stefan T. Ra...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于对比学习在语义和几何任务中的应用，属于计算机视觉领域。虽然对比学习是自监督学习的重要技术，但该标题未提及任何与推荐系统、搜索或广告相关的应用场景，也未涉及Transformer架构、LLM技术或多模态建模。根据您的关注点排除标准，这属于纯粹的视觉论文，与您的技术方向无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:38:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04970v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04970v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We pilot a family of stable contrastive losses for learning pixel-level representations that jointly capture semantic and geometric information. Our approach maps each pixel of an image to an overcomplete descriptor that is both view-invariant and semantically meaningful. It enables precise point-correspondence across images without requiring momentum-based teacher-student training. Two experiments in synthetic 2D and 3D environments demonstrate the properties of our loss and the resulting overcomplete representations.
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            <a href="https://www.alphaxiv.org/abs/2512.04969v1" target="_blank" rel="noopener noreferrer">
                重新思考视觉变换器在AI生成图像检测中的应用
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            Rethinking the Use of Vision Transformers for AI-Generated Image Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>NaHyeon Park, Kunhee Kim, Junsuk Choe, Hyunjung Shim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于AI生成图像检测，这属于AIGC/内容生成领域，被列为不相关主题。虽然提到了视觉变换器，但应用场景是图像检测而非推荐/搜索/广告系统，且没有表明对异构数据建模或推荐系统架构的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:37:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04969v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04969v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Rich feature representations derived from CLIP-ViT have been widely utilized in AI-generated image detection. While most existing methods primarily leverage features from the final layer, we systematically analyze the contributions of layer-wise features to this task. Our study reveals that earlier layers provide more localized and generalizable features, often surpassing the performance of final-layer features in detection tasks. Moreover, we find that different layers capture distinct aspects of the data, each contributing uniquely to AI-generated image detection. Motivated by these findings, we introduce a novel adaptive method, termed MoLD, which dynamically integrates features from multiple ViT layers using a gating-based mechanism. Extensive experiments on both GAN- and diffusion-generated images demonstrate that MoLD significantly improves detection performance, enhances generalization across diverse generative models, and exhibits robustness in real-world scenarios. Finally, we illustrate the scalability and versatility of our approach by successfully applying it to other pre-trained ViTs, such as DINOv2.
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            <a href="https://www.alphaxiv.org/abs/2512.04967v1" target="_blank" rel="noopener noreferrer">
                用于精确视网膜疾病诊断的平衡少样本情景学习
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            Balanced Few-Shot Episodic Learning for Accurate Retinal Disease Diagnosis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jasmaine Khale, Ravi Prakash Srivastava
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域（视网膜疾病诊断），属于明确的无关主题。虽然涉及少样本学习技术，但论文的应用场景是医疗诊断，与推荐系统、搜索或广告领域没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 16:35:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04967v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04967v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Automated retinal disease diagnosis is vital given the rising prevalence of conditions such as diabetic retinopathy and macular degeneration. Conventional deep learning approaches require large annotated datasets, which are costly and often imbalanced across disease categories, limiting their reliability in practice. Few-shot learning (FSL) addresses this challenge by enabling models to generalize from only a few labeled samples per class. In this study,we propose a balanced few-shot episodic learning framework tailored to the Retinal Fundus Multi-Disease Image Dataset (RFMiD). Focusing on the ten most represented classes, which still show substantial imbalance between majority diseases (e.g., Diabetic Retinopathy, Macular Hole) and minority ones (e.g., Optic Disc Edema, Branch Retinal Vein Occlusion), our method integrates three key components: (i) balanced episodic sampling, ensuring equal participation of all classes in each 5-way 5-shot episode; (ii) targeted augmentation, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and color/geometry transformations, to improve minority-class di- versity; and (iii) a ResNet-50 encoder pretrained on ImageNet, selected for its superior ability to capture fine-grained retinal features. Prototypes are computed in the embedding space and classification is performed with cosine similarity for improved stability. Trained on 100 episodes and evaluated on 1,000 test episodes, our framework achieves substantial accuracy gains and reduces bias toward majority classes, with notable improvements for underrepresented diseases. These results demonstrate that dataset-aware few-shot pipelines, combined with balanced sampling and CLAHE-enhanced preprocessing, can deliver more robust and clinically fair retinal disease diagnosis under data-constrained conditions.
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                通过微分同胚螺旋拟合虚拟展开赫库兰尼姆莎草纸卷轴
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            Virtually Unrolling the Herculaneum Papyri by Diffeomorphic Spiral Fitting
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Paul Henderson
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及使用计算机视觉技术处理古代文物（赫库兰尼姆莎草纸卷轴），这属于特定领域应用（考古/文化遗产）。该技术（微分同胚螺旋拟合）是计算机视觉中的图像处理/3D重建方法，但论文没有表明与推荐系统、搜索或广告有任何直接或潜在关联，也不属于核心LLM、Transformer架构进展或异质数据统一建模的范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:57:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04927v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04927v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The Herculaneum Papyri are a collection of rolled papyrus documents that were charred and buried by the famous eruption of Mount Vesuvius. They promise to contain a wealth of previously unseen Greek and Latin texts, but are extremely fragile and thus most cannot be unrolled physically. A solution to access these texts is virtual unrolling, where the papyrus surface is digitally traced out in a CT scan of the scroll, to create a flattened representation. This tracing is very laborious to do manually in gigavoxel-sized scans, so automated approaches are desirable. We present the first top-down method that automatically fits a surface model to a CT scan of a severely damaged scroll. We take a novel approach that globally fits an explicit parametric model of the deformed scroll to existing neural network predictions of where the rolled papyrus likely passes. Our method guarantees the resulting surface is a single continuous 2D sheet, even passing through regions where the surface is not detectable in the CT scan. We conduct comprehensive experiments on high-resolution CT scans of two scrolls, showing that our approach successfully unrolls large regions, and exceeds the performance of the only existing automated unrolling method suitable for this data.
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            <a href="https://www.alphaxiv.org/abs/2512.04890v1" target="_blank" rel="noopener noreferrer">
                面向胎儿磁共振成像的等变对称感知头部姿态估计
            </a>
        </h3>
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        <div class="mb-2 text-base text-gray-700">
            Equivariant Symmetry-Aware Head Pose Estimation for Fetal MRI
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ramya Muthukrishnan, Borjan Gagoski, Aryn Lee, P. Ellen Grant, Elfar Adalsteinss...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（胎儿MRI）中的计算机视觉任务，属于明确的无关主题（医学/生物学领域特定应用）。标题中提到的姿态估计是纯粹的视觉任务，没有展示与推荐系统、搜索或广告的任何潜在联系，也不涉及LLM、Transformer架构或异构数据建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:15:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04890v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04890v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present E(3)-Pose, a novel fast pose estimation method that jointly and explicitly models rotation equivariance and object symmetry. Our work is motivated by the challenging problem of accounting for fetal head motion during a diagnostic MRI scan. We aim to enable automatic adaptive prescription of 2D diagnostic MRI slices with 6-DoF head pose estimation, supported by 3D MRI volumes rapidly acquired before each 2D slice. Existing methods struggle to generalize to clinical volumes, due to pose ambiguities induced by inherent anatomical symmetries, as well as low resolution, noise, and artifacts. In contrast, E(3)-Pose captures anatomical symmetries and rigid pose equivariance by construction, and yields robust estimates of the fetal head pose. Our experiments on publicly available and representative clinical fetal MRI datasets demonstrate the superior robustness and generalization of our method across domains. Crucially, E(3)-Pose achieves state-of-the-art accuracy on clinical MRI volumes, paving the way for clinical translation. Our implementation is available at github.com/ramyamut/E3-Pose.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04888v1" target="_blank" rel="noopener noreferrer">
                仅训练一次（YOTO）：一种无需重新训练的目标检测框架
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            You Only Train Once (YOTO): A Retraining-Free Object Detection Framework
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Priyanto Hidayatullah, Nurjannah Syakrani, Yudi Widhiyasana, Muhammad Rizqi Shol...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的目标检测任务，属于纯粹的视觉研究方向。虽然标题中提到'仅训练一次'可能涉及模型效率，但论文核心是视觉目标检测，与推荐系统、搜索或广告的排名、建模或LLM应用没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:15:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04888v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04888v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Object detection constitutes the primary task within the domain of computer vision. It is utilized in numerous domains. Nonetheless, object detection continues to encounter the issue of catastrophic forgetting. The model must be retrained whenever new products are introduced, utilizing not only the new products dataset but also the entirety of the previous dataset. The outcome is obvious: increasing model training expenses and significant time consumption. In numerous sectors, particularly retail checkout, the frequent introduction of new products presents a great challenge. This study introduces You Only Train Once (YOTO), a methodology designed to address the issue of catastrophic forgetting by integrating YOLO11n for object localization with DeIT and Proxy Anchor Loss for feature extraction and metric learning. For classification, we utilize cosine similarity between the embedding features of the target product and those in the Qdrant vector database. In a case study conducted in a retail store with 140 products, the experimental results demonstrate that our proposed framework achieves encouraging accuracy, whether for detecting new or existing products. Furthermore, without retraining, the training duration difference is significant. We achieve almost 3 times the training time efficiency compared to classical object detection approaches. This efficiency escalates as additional new products are added to the product database. The average inference time is 580 ms per image containing multiple products, on an edge device, validating the proposed framework's feasibility for practical use.
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            <a href="https://www.alphaxiv.org/abs/2512.04883v1" target="_blank" rel="noopener noreferrer">
                SDG-Track：一种面向嵌入式平台高分辨率无人机跟踪的异构观察者-跟随者框架
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            SDG-Track: A Heterogeneous Observer-Follower Framework for High-Resolution UAV Tracking on Embedded Platforms
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiawen Wen, Yu Hu, Suixuan Qiu, Jinshan Huang, Xiaowen Chu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于无人机跟踪的计算机视觉应用，涉及嵌入式平台优化和异构框架设计，与推荐系统、搜索或广告的核心领域进展、LLM技术、Transformer架构或异构数据统一建模均无直接关联。标题中未提及任何与RecSys/Search/Ads相关的技术或潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:11:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04883v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04883v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Real-time tracking of small unmanned aerial vehicles (UAVs) on edge devices faces a fundamental resolution-speed conflict. Downsampling high-resolution imagery to standard detector input sizes causes small target features to collapse below detectable thresholds. Yet processing native 1080p frames on resource-constrained platforms yields insufficient throughput for smooth gimbal control. We propose SDG-Track, a Sparse Detection-Guided Tracker that adopts an Observer-Follower architecture to reconcile this conflict. The Observer stream runs a high-capacity detector at low frequency on the GPU to provide accurate position anchors from 1920x1080 frames. The Follower stream performs high-frequency trajectory interpolation via ROI-constrained sparse optical flow on the CPU. To handle tracking failures from occlusion or model drift caused by spectrally similar distractors, we introduce Dual-Space Recovery, a training-free re-acquisition mechanism combining color histogram matching with geometric consistency constraints. Experiments on a ground-to-air tracking station demonstrate that SDG-Track achieves 35.1 FPS system throughput while retaining 97.2\% of the frame-by-frame detection precision. The system successfully tracks agile FPV drones under real-world operational conditions on an NVIDIA Jetson Orin Nano. Our paper code is publicly available at https://github.com/Jeffry-wen/SDG-Track
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            <a href="https://www.alphaxiv.org/abs/2512.04875v1" target="_blank" rel="noopener noreferrer">
                SP-Det：用于广义多标签病灶检测的自提示双文本融合方法
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            SP-Det: Self-Prompted Dual-Text Fusion for Generalized Multi-Label Lesion Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qing Xu, Yanqian Wang, Xiangjian Hea, Yue Li, Yixuan Zhang, Rong Qu, Wenting Dua...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学影像中的病灶检测（lesion detection），这属于医疗领域特定应用，与用户指定的搜索、推荐、广告等核心领域无关。标题中的“多标签病灶检测”直接表明其医疗应用性质，属于用户明确排除的“Medical, Biology, Chemistry, Physics or other domain-specific applications”类别。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 15:05:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04875v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04875v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Automated lesion detection in chest X-rays has demonstrated significant potential for improving clinical diagnosis by precisely localizing pathological abnormalities. While recent promptable detection frameworks have achieved remarkable accuracy in target localization, existing methods typically rely on manual annotations as prompts, which are labor-intensive and impractical for clinical applications. To address this limitation, we propose SP-Det, a novel self-prompted detection framework that automatically generates rich textual context to guide multi-label lesion detection without requiring expert annotations. Specifically, we introduce an expert-free dual-text prompt generator (DTPG) that leverages two complementary textual modalities: semantic context prompts that capture global pathological patterns and disease beacon prompts that focus on disease-specific manifestations. Moreover, we devise a bidirectional feature enhancer (BFE) that synergistically integrates comprehensive diagnostic context with disease-specific embeddings to significantly improve feature representation and detection accuracy. Extensive experiments on two chest X-ray datasets with diverse thoracic disease categories demonstrate that our SP-Det framework outperforms state-of-the-art detection methods while completely eliminating the dependency on expert-annotated prompts compared to existing promptable architectures.
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            <a href="https://www.alphaxiv.org/abs/2512.04862v1" target="_blank" rel="noopener noreferrer">
                基于生物阻抗传感的人体姿态伪真值接触感知精细化
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            Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Maria-Paola Forte, Nikos Athanasiou, Giulia Ballardini, Jan Ulrich Bartels, Kath...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于通过生物阻抗传感改进人体姿态估计，属于计算机视觉/生物医学工程领域，与推荐系统、搜索或广告的核心技术无直接关联。即使考虑异构数据建模，该技术主要针对物理传感器数据，而非推荐/搜索/广告中典型的用户行为、上下文特征等数据模态，缺乏明确的跨领域应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:45:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04862v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04862v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Capturing accurate 3D human pose in the wild would provide valuable data for training pose estimation and motion generation methods. While video-based estimation approaches have become increasingly accurate, they often fail in common scenarios involving self-contact, such as a hand touching the face. In contrast, wearable bioimpedance sensing can cheaply and unobtrusively measure ground-truth skin-to-skin contact. Consequently, we propose a novel framework that combines visual pose estimators with bioimpedance sensing to capture the 3D pose of people by taking self-contact into account. Our method, BioTUCH, initializes the pose using an off-the-shelf estimator and introduces contact-aware pose optimization during measured self-contact: reprojection error and deviations from the input estimate are minimized while enforcing vertex proximity constraints. We validate our approach using a new dataset of synchronized RGB video, bioimpedance measurements, and 3D motion capture. Testing with three input pose estimators, we demonstrate an average of 11.7% improvement in reconstruction accuracy. We also present a miniature wearable bioimpedance sensor that enables efficient large-scale collection of contact-aware training data for improving pose estimation and generation using BioTUCH. Code and data are available at biotuch.is.tue.mpg.de
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            <a href="https://www.alphaxiv.org/abs/2512.04837v1" target="_blank" rel="noopener noreferrer">
                现实世界中多域人脸伪造检测的合理性检验
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
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        <div class="mb-2 text-base text-gray-700">
            A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jikang Cheng, Renye Yan, Zhiyuan Yan, Yaozhong Gan, Xueyi Zhang, Zhongyuan Wang,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于人脸伪造检测，属于计算机视觉中的特定应用领域，与推荐系统、搜索或广告的核心技术无关。标题中提到的'多域'指的是不同伪造方法或数据集，而非推荐/搜索/广告中所需的异构数据模态统一建模。该研究缺乏明确的潜在应用指向推荐系统、搜索或广告领域的技术或架构。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:21:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04837v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04837v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing methods for deepfake detection aim to develop generalizable detectors. Although "generalizable" is the ultimate target once and for all, with limited training forgeries and domains, it appears idealistic to expect generalization that covers entirely unseen variations, especially given the diversity of real-world deepfakes. Therefore, introducing large-scale multi-domain data for training can be feasible and important for real-world applications. However, within such a multi-domain scenario, the differences between multiple domains, rather than the subtle real/fake distinctions, dominate the feature space. As a result, despite detectors being able to relatively separate real and fake within each domain (i.e., high AUC), they struggle with single-image real/fake judgments in domain-unspecified conditions (i.e., low ACC). In this paper, we first define a new research paradigm named Multi-In-Domain Face Forgery Detection (MID-FFD), which includes sufficient volumes of real-fake domains for training. Then, the detector should provide definitive real-fake judgments to the domain-unspecified inputs, which simulate the frame-by-frame independent detection scenario in the real world. Meanwhile, to address the domain-dominant issue, we propose a model-agnostic framework termed DevDet (Developer for Detector) to amplify real/fake differences and make them dominant in the feature space. DevDet consists of a Face Forgery Developer (FFDev) and a Dose-Adaptive detector Fine-Tuning strategy (DAFT). Experiments demonstrate our superiority in predicting real-fake under the MID-FFD scenario while maintaining original generalization ability to unseen data.
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            <a href="https://www.alphaxiv.org/abs/2512.04821v1" target="_blank" rel="noopener noreferrer">
                LatentFM：一种基于潜在流匹配的生成式医学图像分割方法
            </a>
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            LatentFM: A Latent Flow Matching Approach for Generative Medical Image Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huynh Trinh Ngoc, Hoang Anh Nguyen Kim, Toan Nguyen Hai, Long Tran Quoc
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学图像分割，属于明确的医学领域应用，与用户指定的无关主题中的'Medical, Biology, Chemistry, Physics or other domain-specific applications'直接冲突。虽然标题提及'Latent Flow Matching'这一生成式方法，但其应用场景（医学图像）使其与用户关注的推荐系统、搜索、广告等核心领域完全无关，因此不具备相关性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:06:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04821v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04821v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generative models have achieved remarkable progress with the emergence of flow matching (FM). It has demonstrated strong generative capabilities and attracted significant attention as a simulation-free flow-based framework capable of learning exact data densities. Motivated by these advances, we propose LatentFM, a flow-based model operating in the latent space for medical image segmentation. To model the data distribution, we first design two variational autoencoders (VAEs) to encode both medical images and their corresponding masks into a lower-dimensional latent space. We then estimate a conditional velocity field that guides the flow based on the input image. By sampling multiple latent representations, our method synthesizes diverse segmentation outputs whose pixel-wise variance reliably captures the underlying data distribution, enabling both highly accurate and uncertainty-aware predictions. Furthermore, we generate confidence maps that quantify the model certainty, providing clinicians with richer information for deeper analysis. We conduct experiments on two datasets, ISIC-2018 and CVC-Clinic, and compare our method with several prior baselines, including both deterministic and generative approach models. Through comprehensive evaluations, both qualitative and quantitative results show that our approach achieves superior segmentation accuracy while remaining highly efficient in the latent space.
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            <a href="https://www.alphaxiv.org/abs/2512.04815v1" target="_blank" rel="noopener noreferrer">
                RobustSplat++：解耦致密化、动态与光照以实现野外环境下的3D高斯溅射
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        <div class="mb-2 text-base text-gray-700">
            RobustSplat++: Decoupling Densification, Dynamics, and Illumination for In-the-Wild 3DGS
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chuanyu Fu, Guanying Chen, Yuqi Zhang, Kunbin Yao, Yuan Xiong, Chuan Huang, Shug...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D高斯溅射（3DGS）在野外场景下的技术改进，属于纯粹的计算机视觉和3D重建领域。虽然涉及多模态数据处理（动态、光照），但其核心是视觉几何重建技术，与推荐系统、搜索或广告的排序、匹配、用户建模等核心问题没有直接关联，也不属于Transformer架构或LLM技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:05:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04815v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04815v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D Gaussian Splatting (3DGS) has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling. However, existing methods struggle with accurately modeling in-the-wild scenes affected by transient objects and illuminations, leading to artifacts in the rendered images. We identify that the Gaussian densification process, while enhancing scene detail capture, unintentionally contributes to these artifacts by growing additional Gaussians that model transient disturbances and illumination variations. To address this, we propose RobustSplat++, a robust solution based on several critical designs. First, we introduce a delayed Gaussian growth strategy that prioritizes optimizing static scene structure before allowing Gaussian splitting/cloning, mitigating overfitting to transient objects in early optimization. Second, we design a scale-cascaded mask bootstrapping approach that first leverages lower-resolution feature similarity supervision for reliable initial transient mask estimation, taking advantage of its stronger semantic consistency and robustness to noise, and then progresses to high-resolution supervision to achieve more precise mask prediction. Third, we incorporate the delayed Gaussian growth strategy and mask bootstrapping with appearance modeling to handling in-the-wild scenes including transients and illuminations. Extensive experiments on multiple challenging datasets show that our method outperforms existing methods, clearly demonstrating the robustness and effectiveness of our method.
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            <a href="https://www.alphaxiv.org/abs/2512.04814v1" target="_blank" rel="noopener noreferrer">
                用于人脸-语音关联的共享多模态嵌入空间
            </a>
        </h3>
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        <div class="mb-2 text-base text-gray-700">
            Shared Multi-modal Embedding Space for Face-Voice Association
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Christopher Simic, Korbinian Riedhammer, Tobias Bocklet
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人脸与语音两种模态的关联建模，属于纯粹的跨模态表示学习范畴。虽然涉及多模态嵌入技术，但人脸和语音模态与推荐系统、搜索或广告中的异构数据（如用户序列、上下文特征）没有直接关联，且论文未提及任何在RecSys/Search/Ads领域的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 14:04:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04814v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04814v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.CV</span></div>
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                    The FAME 2026 challenge comprises two demanding tasks: training face-voice associations combined with a multilingual setting that includes testing on languages on which the model was not trained. Our approach consists of separate uni-modal processing pipelines with general face and voice feature extraction, complemented by additional age-gender feature extraction to support prediction. The resulting single-modal features are projected into a shared embedding space and trained with an Adaptive Angular Margin (AAM) loss. Our approach achieved first place in the FAME 2026 challenge, with an average Equal-Error Rate (EER) of 23.99%.
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            <a href="https://www.alphaxiv.org/abs/2512.04786v1" target="_blank" rel="noopener noreferrer">
                LaFiTe：用于原生三维纹理生成的生成式潜在场
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            LaFiTe: A Generative Latent Field for 3D Native Texturing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chia-Hao Chen, Zi-Xin Zou, Yan-Pei Cao, Ze Yuan, Guan Luo, Xiaojuan Qi, Ding Lia...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于3D纹理生成和计算机图形学领域，属于纯粹的视觉/图形研究方向。虽然标题中提到“生成式”和“潜在场”等技术概念，但论文的核心应用场景（3D原生纹理）与推荐系统、搜索或广告领域没有直接关联，也不符合您关注的LLM技术、Transformer架构进展或异构数据统一建模等研究方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 13:33:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04786v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04786v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating high-fidelity, seamless textures directly on 3D surfaces, what we term 3D-native texturing, remains a fundamental open challenge, with the potential to overcome long-standing limitations of UV-based and multi-view projection methods. However, existing native approaches are constrained by the absence of a powerful and versatile latent representation, which severely limits the fidelity and generality of their generated textures. We identify this representation gap as the principal barrier to further progress. We introduce LaFiTe, a framework that addresses this challenge by learning to generate textures as a 3D generative sparse latent color field. At its core, LaFiTe employs a variational autoencoder (VAE) to encode complex surface appearance into a sparse, structured latent space, which is subsequently decoded into a continuous color field. This representation achieves unprecedented fidelity, exceeding state-of-the-art methods by >10 dB PSNR in reconstruction, by effectively disentangling texture appearance from mesh topology and UV parameterization. Building upon this strong representation, a conditional rectified-flow model synthesizes high-quality, coherent textures across diverse styles and geometries. Extensive experiments demonstrate that LaFiTe not only sets a new benchmark for 3D-native texturing but also enables flexible downstream applications such as material synthesis and texture super-resolution, paving the way for the next generation of 3D content creation workflows.
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            <a href="https://www.alphaxiv.org/abs/2512.04761v1" target="_blank" rel="noopener noreferrer">
                顺序至关重要：基于序列化VR草图的3D形状生成
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            Order Matters: 3D Shape Generation from Sequential VR Sketches
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yizi Chen, Sidi Wu, Tianyi Xiao, Nina Wiedemann, Loic Landrieu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D形状生成和VR草图技术，属于纯粹的计算机视觉和图形学领域。标题中提到的序列化输入可能涉及顺序建模，但这与推荐系统、搜索或广告中的用户行为序列建模有本质区别。该研究没有展示与推荐、搜索或广告领域的潜在应用关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:53:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04761v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04761v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    VR sketching lets users explore and iterate on ideas directly in 3D, offering a faster and more intuitive alternative to conventional CAD tools. However, existing sketch-to-shape models ignore the temporal ordering of strokes, discarding crucial cues about structure and design intent. We introduce VRSketch2Shape, the first framework and multi-category dataset for generating 3D shapes from sequential VR sketches. Our contributions are threefold: (i) an automated pipeline that generates sequential VR sketches from arbitrary shapes, (ii) a dataset of over 20k synthetic and 900 hand-drawn sketch-shape pairs across four categories, and (iii) an order-aware sketch encoder coupled with a diffusion-based 3D generator. Our approach yields higher geometric fidelity than prior work, generalizes effectively from synthetic to real sketches with minimal supervision, and performs well even on partial sketches. All data and models will be released open-source at https://chenyizi086.github.io/VRSketch2Shape_website.
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            <a href="https://www.alphaxiv.org/abs/2512.04734v1" target="_blank" rel="noopener noreferrer">
                MT-Depth：面向深度补全的多任务实例特征分析
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            MT-Depth: Multi-task Instance feature analysis for the Depth Completion
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abdul Haseeb Nizamani, Dandi Zhou, Xinhai Sun
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于计算机视觉中的深度补全任务，属于纯粹的3D视觉研究范畴。根据您的关注点排除标准，纯粹的视觉或3D视觉论文若未明确展示与推荐系统、搜索或广告的相关性，应被视为不相关。该标题未提及任何可能应用于推荐、搜索或广告领域的多任务学习或特征分析技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:17:33
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                <a href="https://arxiv.org/abs/2512.04734v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04734v1
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                    Depth completion plays a vital role in 3D perception systems, especially in scenarios where sparse depth data must be densified for tasks such as autonomous driving, robotics, and augmented reality. While many existing approaches rely on semantic segmentation to guide depth completion, they often overlook the benefits of object-level understanding. In this work, we introduce an instance-aware depth completion framework that explicitly integrates binary instance masks as spatial priors to refine depth predictions. Our model combines four main components: a frozen YOLO V11 instance segmentation branch, a U-Net-based depth completion backbone, a cross-attention fusion module, and an attention-guided prediction head. The instance segmentation branch generates per-image foreground masks that guide the depth branch via cross-attention, allowing the network to focus on object-centric regions during refinement. We validate our method on the Virtual KITTI 2 dataset, showing that it achieves lower RMSE compared to both a U-Net-only baseline and previous semantic-guided methods, while maintaining competitive MAE. Qualitative and quantitative results demonstrate that the proposed model effectively enhances depth accuracy near object boundaries, occlusions, and thin structures. Our findings suggest that incorporating instance-aware cues offers a promising direction for improving depth completion without relying on dense semantic labels.
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            <a href="https://www.alphaxiv.org/abs/2512.04733v1" target="_blank" rel="noopener noreferrer">
                E3AD：一种面向以人为中心的端到端自动驾驶的情感感知视觉-语言-动作模型
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            E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yihong Tang, Haicheng Liao, Tong Nie, Junlin He, Ao Qu, Kehua Chen, Wei Ma, Zhen...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于自动驾驶领域，属于计算机视觉和机器人学的交叉应用。虽然提到了“Vision-Language-Action”模型，但其核心应用场景（自动驾驶）与用户推荐系统、搜索或广告的排名任务无直接关联，且未暗示任何可迁移至这些领域的通用技术或架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:17:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04733v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04733v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    End-to-end autonomous driving (AD) systems increasingly adopt vision-language-action (VLA) models, yet they typically ignore the passenger's emotional state, which is central to comfort and AD acceptance. We introduce Open-Domain End-to-End (OD-E2E) autonomous driving, where an autonomous vehicle (AV) must interpret free-form natural-language commands, infer the emotion, and plan a physically feasible trajectory. We propose E3AD, an emotion-aware VLA framework that augments semantic understanding with two cognitively inspired components: a continuous Valenc-Arousal-Dominance (VAD) emotion model that captures tone and urgency from language, and a dual-pathway spatial reasoning module that fuses egocentric and allocentric views for human-like spatial cognition. A consistency-oriented training scheme, combining modality pretraining with preference-based alignment, further enforces coherence between emotional intent and driving actions. Across real-world datasets, E3AD improves visual grounding and waypoint planning and achieves state-of-the-art (SOTA) VAD correlation for emotion estimation. These results show that injecting emotion into VLA-style driving yields more human-aligned grounding, planning, and human-centric feedback.
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            <a href="https://www.alphaxiv.org/abs/2512.04728v1" target="_blank" rel="noopener noreferrer">
                测量未言之语：一种用于真实场景心理分析的解耦模型与基准
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            Measuring the Unspoken: A Disentanglement Model and Benchmark for Psychological Analysis in the Wild
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yigui Feng, Qinglin Wang, Haotian Mo, Yang Liu, Ke Liu, Gencheng Liu, Xinhai Che...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于心理学分析和解耦模型，属于特定领域应用（心理学），与推荐系统、搜索或广告的核心技术进展无关。标题中提到的'基准'可能涉及评估，但这属于纯粹NLP或心理学领域，没有显示对推荐/搜索/广告技术的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 12:13:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04728v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04728v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Generative psychological analysis of in-the-wild conversations faces two fundamental challenges: (1) existing Vision-Language Models (VLMs) fail to resolve Articulatory-Affective Ambiguity, where visual patterns of speech mimic emotional expressions; and (2) progress is stifled by a lack of verifiable evaluation metrics capable of assessing visual grounding and reasoning depth. We propose a complete ecosystem to address these twin challenges. First, we introduce Multilevel Insight Network for Disentanglement(MIND), a novel hierarchical visual encoder that introduces a Status Judgment module to algorithmically suppress ambiguous lip features based on their temporal feature variance, achieving explicit visual disentanglement. Second, we construct ConvoInsight-DB, a new large-scale dataset with expert annotations for micro-expressions and deep psychological inference. Third, Third, we designed the Mental Reasoning Insight Rating Metric (PRISM), an automated dimensional framework that uses expert-guided LLM to measure the multidimensional performance of large mental vision models. On our PRISM benchmark, MIND significantly outperforms all baselines, achieving a +86.95% gain in micro-expression detection over prior SOTA. Ablation studies confirm that our Status Judgment disentanglement module is the most critical component for this performance leap. Our code has been opened.
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            <a href="https://www.alphaxiv.org/abs/2512.04705v1" target="_blank" rel="noopener noreferrer">
                面向边缘加速器的硬件感知型早期退出网络神经架构搜索
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            Hardware-aware Neural Architecture Search of Early Exiting Networks on Edge Accelerators
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alaa Zniber, Arne Symons, Ouassim Karrakchou, Marian Verhelst, Mounir Ghogho
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及神经架构搜索（NAS），属于明确排除的无关主题。虽然提到了边缘加速器可能暗示效率改进，但核心是NAS而非Transformer架构效率或LLM技术。没有迹象表明该技术直接适用于推荐系统、搜索或广告领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:54:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04705v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04705v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CC</span><span class="category-tag">cs.AR</span><span class="category-tag">cs.CV</span></div>
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                    Advancements in high-performance computing and cloud technologies have enabled the development of increasingly sophisticated Deep Learning (DL) models. However, the growing demand for embedded intelligence at the edge imposes stringent computational and energy constraints, challenging the deployment of these large-scale models. Early Exiting Neural Networks (EENN) have emerged as a promising solution, allowing dynamic termination of inference based on input complexity to enhance efficiency. Despite their potential, EENN performance is highly influenced by the heterogeneity of edge accelerators and the constraints imposed by quantization, affecting accuracy, energy efficiency, and latency. Yet, research on the automatic optimization of EENN design for edge hardware remains limited. To bridge this gap, we propose a hardware-aware Neural Architecture Search (NAS) framework that systematically integrates the effects of quantization and hardware resource allocation to optimize the placement of early exit points within a network backbone. Experimental results on the CIFAR-10 dataset demonstrate that our NAS framework can discover architectures that achieve over a 50\% reduction in computational costs compared to conventional static networks, making them more suitable for deployment in resource-constrained edge environments.
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            <a href="https://www.alphaxiv.org/abs/2512.04699v1" target="_blank" rel="noopener noreferrer">
                OmniScaleSR：释放尺度可控扩散先验，实现忠实且逼真的任意尺度图像超分辨率
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            OmniScaleSR: Unleashing Scale-Controlled Diffusion Prior for Faithful and Realistic Arbitrary-Scale Image Super-Resolution
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinning Chai, Zhengxue Cheng, Yuhong Zhang, Hengsheng Zhang, Yingsheng Qin, Yuca...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像超分辨率技术，属于纯粹的视觉处理任务，与推荐系统、搜索或广告的排名核心没有直接关联。虽然扩散模型是生成式AI的重要技术，但论文的应用场景（图像超分辨率）属于AIGC/内容生成范畴，不符合当前关注的直接LLM应用或异构数据统一建模方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:50:17
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                <a href="https://arxiv.org/abs/2512.04699v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04699v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Arbitrary-scale super-resolution (ASSR) overcomes the limitation of traditional super-resolution (SR) methods that operate only at fixed scales (e.g., 4x), enabling a single model to handle arbitrary magnification. Most existing ASSR approaches rely on implicit neural representation (INR), but its regression-driven feature extraction and aggregation intrinsically limit the ability to synthesize fine details, leading to low realism. Recent diffusion-based realistic image super-resolution (Real-ISR) models leverage powerful pre-trained diffusion priors and show impressive results at the 4x setting. We observe that they can also achieve ASSR because the diffusion prior implicitly adapts to scale by encouraging high-realism generation. However, without explicit scale control, the diffusion process cannot be properly adjusted for different magnification levels, resulting in excessive hallucination or blurry outputs, especially under ultra-high scales. To address these issues, we propose OmniScaleSR, a diffusion-based realistic arbitrary-scale SR framework designed to achieve both high fidelity and high realism. We introduce explicit, diffusion-native scale control mechanisms that work synergistically with implicit scale adaptation, enabling scale-aware and content-aware modulation of the diffusion process. In addition, we incorporate multi-domain fidelity enhancement designs to further improve reconstruction accuracy. Extensive experiments on bicubic degradation benchmarks and real-world datasets show that OmniScaleSR surpasses state-of-the-art methods in both fidelity and perceptual realism, with particularly strong performance at large magnification factors. Code will be released at https://github.com/chaixinning/OmniScaleSR.
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            <a href="https://www.alphaxiv.org/abs/2512.04677v1" target="_blank" rel="noopener noreferrer">
                实时虚拟化身：基于无限时长实时音频驱动的流式虚拟化身生成
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            Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yubo Huang, Hailong Guo, Fangtai Wu, Shifeng Zhang, Shijie Huang, Qijun Gan, Lin...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于实时音频驱动的虚拟化身生成，属于计算机视觉和图形学领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然涉及实时处理，但未体现对推荐、搜索或广告场景的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 11:11:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04677v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04677v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing diffusion-based video generation methods are fundamentally constrained by sequential computation and long-horizon inconsistency, limiting their practical adoption in real-time, streaming audio-driven avatar synthesis. We present Live Avatar, an algorithm-system co-designed framework that enables efficient, high-fidelity, and infinite-length avatar generation using a 14-billion-parameter diffusion model. Our approach introduces Timestep-forcing Pipeline Parallelism (TPP), a distributed inference paradigm that pipelines denoising steps across multiple GPUs, effectively breaking the autoregressive bottleneck and ensuring stable, low-latency real-time streaming. To further enhance temporal consistency and mitigate identity drift and color artifacts, we propose the Rolling Sink Frame Mechanism (RSFM), which maintains sequence fidelity by dynamically recalibrating appearance using a cached reference image. Additionally, we leverage Self-Forcing Distribution Matching Distillation to facilitate causal, streamable adaptation of large-scale models without sacrificing visual quality. Live Avatar demonstrates state-of-the-art performance, reaching 20 FPS end-to-end generation on 5 H800 GPUs, and, to the best of our knowledge, is the first to achieve practical, real-time, high-fidelity avatar generation at this scale. Our work establishes a new paradigm for deploying advanced diffusion models in industrial long-form video synthesis applications.
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            <a href="https://www.alphaxiv.org/abs/2512.04660v1" target="_blank" rel="noopener noreferrer">
                I2I-Bench：图像到图像编辑模型的综合基准套件
            </a>
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            I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Juntong Wang, Jiarui Wang, Huiyu Duan, Jiaxiang Kang, Guangtao Zhai, Xiongkuo Mi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像编辑模型的基准测试，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无关。虽然图像编辑技术可能间接应用于广告创意生成，但这属于明确排除的非排名广告主题范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 10:44:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04660v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04660v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Image editing models are advancing rapidly, yet comprehensive evaluation remains a significant challenge. Existing image editing benchmarks generally suffer from limited task scopes, insufficient evaluation dimensions, and heavy reliance on manual annotations, which significantly constrain their scalability and practical applicability. To address this, we propose \textbf{I2I-Bench}, a comprehensive benchmark for image-to-image editing models, which features (i) diverse tasks, encompassing 10 task categories across both single-image and multi-image editing tasks, (ii) comprehensive evaluation dimensions, including 30 decoupled and fine-grained evaluation dimensions with automated hybrid evaluation methods that integrate specialized tools and large multimodal models (LMMs), and (iii) rigorous alignment validation, justifying the consistency between our benchmark evaluations and human preferences. Using I2I-Bench, we benchmark numerous mainstream image editing models, investigating the gaps and trade-offs between editing models across various dimensions. We will open-source all components of I2I-Bench to facilitate future research.
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            <a href="https://www.alphaxiv.org/abs/2512.04599v1" target="_blank" rel="noopener noreferrer">
                基于视觉-语言分割融合的恶意图像分析：单次检测、元素识别与定位
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            Malicious Image Analysis via Vision-Language Segmentation Fusion: Detection, Element, and Location in One-shot
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sheng Hang, Chaoxiang He, Hongsheng Hu, Hanqing Hu, Bin Benjamin Zhu, Shi-Feng S...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于恶意图像分析，属于计算机视觉安全领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及视觉-语言模型，但其应用场景（恶意图像检测）属于安全范畴，属于明确排除的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 09:18:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04599v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04599v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Detecting illicit visual content demands more than image-level NSFW flags; moderators must also know what objects make an image illegal and where those objects occur. We introduce a zero-shot pipeline that simultaneously (i) detects if an image contains harmful content, (ii) identifies each critical element involved, and (iii) localizes those elements with pixel-accurate masks - all in one pass. The system first applies foundation segmentation model (SAM) to generate candidate object masks and refines them into larger independent regions. Each region is scored for malicious relevance by a vision-language model using open-vocabulary prompts; these scores weight a fusion step that produces a consolidated malicious object map. An ensemble across multiple segmenters hardens the pipeline against adaptive attacks that target any single segmentation method. Evaluated on a newly-annotated 790-image dataset spanning drug, sexual, violent and extremist content, our method attains 85.8% element-level recall, 78.1% precision and a 92.1% segment-success rate - exceeding direct zero-shot VLM localization by 27.4% recall at comparable precision. Against PGD adversarial perturbations crafted to break SAM and VLM, our method's precision and recall decreased by no more than 10%, demonstrating high robustness against attacks. The full pipeline processes an image in seconds, plugs seamlessly into existing VLM workflows, and constitutes the first practical tool for fine-grained, explainable malicious-image moderation.
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            <a href="https://www.alphaxiv.org/abs/2512.04597v1" target="_blank" rel="noopener noreferrer">
                机器人何时应说“我不知道”：具身问答中弃权行为的基准测试
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            When Robots Should Say "I Don't Know": Benchmarking Abstention in Embodied Question Answering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Wu, Chuhao Zhou, Guangyu Zhao, Haozhi Cao, Yewen Pu, Jianfei Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于具身问答中的弃权行为基准测试，属于机器人学和具体环境交互领域。虽然涉及问答系统，但核心是具身智能和机器人决策，与推荐系统、搜索或广告的排名、建模或架构进步没有直接关联，也不属于LLM或Transformer技术的核心进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 09:17:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04597v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04597v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                    Embodied Question Answering (EQA) requires an agent to interpret language, perceive its environment, and navigate within 3D scenes to produce responses. Existing EQA benchmarks assume that every question must be answered, but embodied agents should know when they do not have sufficient information to answer. In this work, we focus on a minimal requirement for EQA agents, abstention: knowing when to withhold an answer. From an initial study of 500 human queries, we find that 32.4% contain missing or underspecified context. Drawing on this initial study and cognitive theories of human communication errors, we derive five representative categories requiring abstention: actionability limitation, referential underspecification, preference dependence, information unavailability, and false presupposition. We augment OpenEQA by having annotators transform well-posed questions into ambiguous variants outlined by these categories. The resulting dataset, AbstainEQA, comprises 1,636 annotated abstention cases paired with 1,636 original OpenEQA instances for balanced evaluation. Evaluating on AbstainEQA, we find that even the best frontier model only attains 42.79% abstention recall, while humans achieve 91.17%. We also find that scaling, prompting, and reasoning only yield marginal gains, and that fine-tuned models overfit to textual cues. Together, these results position abstention as a fundamental prerequisite for reliable interaction in embodied settings and as a necessary basis for effective clarification.
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            <a href="https://www.alphaxiv.org/abs/2512.04576v1" target="_blank" rel="noopener noreferrer">
                TARDis：基于时间衰减表示解耦的不完整多模态肿瘤分割与分类
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            TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classification
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zishuo Wan, Qinqin Kang, Yi Huang, Yun Bian, Dawei Ding, Ke Yan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学影像领域（肿瘤分割与分类），这属于明确的无关主题。虽然提到了多模态数据和表示解耦，但其应用场景是医学肿瘤分析，与推荐系统、搜索或广告领域没有任何关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:44:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04576v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04576v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Tumor segmentation and diagnosis in contrast-enhanced Computed Tomography (CT) rely heavily on the physiological dynamics of contrast agents. However, obtaining a complete multi-phase series is often clinically unfeasible due to radiation concerns or scanning limitations, leading to the "missing modality" problem. Existing deep learning approaches typically treat missing phases as absent independent channels, ignoring the inherent temporal continuity of hemodynamics. In this work, we propose Time Attenuated Representation Disentanglement (TARDis), a novel physics-aware framework that redefines missing modalities as missing sample points on a continuous Time-Attenuation Curve. TARDis explicitly disentangles the latent feature space into a time-invariant static component (anatomy) and a time-dependent dynamic component (perfusion). We achieve this via a dual-path architecture: a quantization-based path using a learnable embedding dictionary to extract consistent anatomical structures, and a probabilistic path using a Conditional Variational Autoencoder to model dynamic enhancement conditioned on the estimated scan time. This design allows the network to hallucinate missing hemodynamic features by sampling from the learned latent distribution. Extensive experiments on a large-scale private abdominal CT dataset (2,282 cases) and two public datasets demonstrate that TARDis significantly outperforms state-of-the-art incomplete modality frameworks. Notably, our method maintains robust diagnostic performance even in extreme data-sparsity scenarios, highlighting its potential for reducing radiation exposure while maintaining diagnostic precision.
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            <a href="https://www.alphaxiv.org/abs/2512.04564v1" target="_blank" rel="noopener noreferrer">
                基于监督深度学习分析的显微图像数据集创建——重要考量因素与建议综述
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            Dataset creation for supervised deep learning-based analysis of microscopic images - review of important considerations and recommendations
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Christof A. Bertram, Viktoria Weiss, Jonas Ammeling, F. Maria Schabel, Taryn A. ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学/生物学领域的显微图像数据集创建，属于明确的无关主题“Medical, Biology, Chemistry, Physics or other domain-specific applications”。论文内容涉及监督深度学习的数据准备，与推荐系统、搜索、广告或相关使能技术无直接关联，也没有展示出在这些领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:27:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04564v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04564v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Supervised deep learning (DL) receives great interest for automated analysis of microscopic images with an increasing body of literature supporting its potential. The development and validation of those DL models relies heavily on the availability of high-quality, large-scale datasets. However, creating such datasets is a complex and resource-intensive process, often hindered by challenges such as time constraints, domain variability, and risks of bias in image collection and label creation. This review provides a comprehensive guide to the critical steps in dataset creation, including: 1) image acquisition, 2) selection of annotation software, and 3) annotation creation. In addition to ensuring a sufficiently large number of images, it is crucial to address sources of image variability (domain shifts) - such as those related to slide preparation and digitization - that could lead to algorithmic errors if not adequately represented in the training data. Key quality criteria for annotations are the three "C"s: correctness, completeness, and consistency. This review explores methods to enhance annotation quality through the use of advanced techniques that mitigate the limitations of single annotators. To support dataset creators, a standard operating procedure (SOP) is provided as supplemental material, outlining best practices for dataset development. Furthermore, the article underscores the importance of open datasets in driving innovation and enhancing reproducibility of DL research. By addressing the challenges and offering practical recommendations, this review aims to advance the creation of and availability to high-quality, large-scale datasets, ultimately contributing to the development of generalizable and robust DL models for pathology applications.
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                通过可微分稀疏核复数的空间变体卷积高效实现
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            Efficient Spatially-Variant Convolution via Differentiable Sparse Kernel Complex
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhizhen Wu, Zhe Cao, Yuchi Huo
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的高效卷积方法，属于纯视觉技术范畴。虽然提到了可微分和高效实现，但没有明确连接推荐系统、搜索或广告应用，也不涉及Transformer架构、LLM技术或多模态建模。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 08:20:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04556v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04556v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.GR</span><span class="category-tag">cs.CV</span></div>
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                    Image convolution with complex kernels is a fundamental operation in photography, scientific imaging, and animation effects, yet direct dense convolution is computationally prohibitive on resource-limited devices. Existing approximations, such as simulated annealing or low-rank decompositions, either lack efficiency or fail to capture non-convex kernels. We introduce a differentiable kernel decomposition framework that represents a target spatially-variant, dense, complex kernel using a set of sparse kernel samples. Our approach features (i) a decomposition that enables differentiable optimization of sparse kernels, (ii) a dedicated initialization strategy for non-convex shapes to avoid poor local minima, and (iii) a kernel-space interpolation scheme that extends single-kernel filtering to spatially varying filtering without retraining and additional runtime overhead. Experiments on Gaussian and non-convex kernels show that our method achieves higher fidelity than simulated annealing and significantly lower cost than low-rank decompositions. Our approach provides a practical solution for mobile imaging and real-time rendering, while remaining fully differentiable for integration into broader learning pipelines.
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            <a href="https://www.alphaxiv.org/abs/2512.04542v1" target="_blank" rel="noopener noreferrer">
                高斯熵场：驱动三维高斯优化中的自适应稀疏性
            </a>
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            Gaussian Entropy Fields: Driving Adaptive Sparsity in 3D Gaussian Optimization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hong Kuang, Jianchen Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文标题涉及三维高斯优化和自适应稀疏性技术，这属于计算机视觉/图形学领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然优化技术可能具有通用性，但标题没有表明任何在推荐/搜索/广告领域的潜在应用，因此相关性极低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:44:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04542v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04542v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D Gaussian Splatting (3DGS) has emerged as a leading technique for novel view synthesis, demonstrating exceptional rendering efficiency. \replaced[]{Well-reconstructed surfaces can be characterized by low configurational entropy, where dominant primitives clearly define surface geometry while redundant components are suppressed.}{The key insight is that well-reconstructed surfaces naturally exhibit low configurational entropy, where dominant primitives clearly define surface geometry while suppressing redundant components.} Three complementary technical contributions are introduced: (1) entropy-driven surface modeling via entropy minimization for low configurational entropy in primitive distributions; (2) adaptive spatial regularization using the Surface Neighborhood Redundancy Index (SNRI) and image entropy-guided weighting; (3) multi-scale geometric preservation through competitive cross-scale entropy alignment. Extensive experiments demonstrate that GEF achieves competitive geometric precision on DTU and T\&T benchmarks, while delivering superior rendering quality compared to existing methods on Mip-NeRF 360. Notably, superior Chamfer Distance (0.64) on DTU and F1 score (0.44) on T\&T are obtained, alongside the best SSIM (0.855) and LPIPS (0.136) among baselines on Mip-NeRF 360, validating the framework's ability to enhance surface reconstruction accuracy without compromising photometric fidelity.
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            <a href="https://www.alphaxiv.org/abs/2512.04537v1" target="_blank" rel="noopener noreferrer">
                X-Humanoid：通过将人类视频机器人化以大规模生成人形机器人视频
            </a>
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            X-Humanoid: Robotize Human Videos to Generate Humanoid Videos at Scale
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pei Yang, Hai Ci, Yiren Song, Mike Zheng Shou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及机器人技术和视频生成，属于纯粹的计算机视觉/机器人领域，与推荐系统、搜索或广告的核心技术焦点无关。标题中提到的“大规模生成”可能涉及数据生成技术，但这与RecSys/Search/Ads中处理用户行为、内容特征或序列数据的核心问题没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:34:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04537v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04537v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The advancement of embodied AI has unlocked significant potential for intelligent humanoid robots. However, progress in both Vision-Language-Action (VLA) models and world models is severely hampered by the scarcity of large-scale, diverse training data. A promising solution is to "robotize" web-scale human videos, which has been proven effective for policy training. However, these solutions mainly "overlay" robot arms to egocentric videos, which cannot handle complex full-body motions and scene occlusions in third-person videos, making them unsuitable for robotizing humans. To bridge this gap, we introduce X-Humanoid, a generative video editing approach that adapts the powerful Wan 2.2 model into a video-to-video structure and finetunes it for the human-to-humanoid translation task. This finetuning requires paired human-humanoid videos, so we designed a scalable data creation pipeline, turning community assets into 17+ hours of paired synthetic videos using Unreal Engine. We then apply our trained model to 60 hours of the Ego-Exo4D videos, generating and releasing a new large-scale dataset of over 3.6 million "robotized" humanoid video frames. Quantitative analysis and user studies confirm our method's superiority over existing baselines: 69% of users rated it best for motion consistency, and 62.1% for embodiment correctness.
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            <a href="https://www.alphaxiv.org/abs/2512.04536v1" target="_blank" rel="noopener noreferrer">
                基于循环融合模型从面部视频序列检测醉酒个体
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            Detection of Intoxicated Individuals from Facial Video Sequences via a Recurrent Fusion Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bita Baroutian, Atefe Aghaei, Mohsen Ebrahimi Moghaddam
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于从面部视频序列检测醉酒个体，这属于计算机视觉和生物识别领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及序列建模，但其应用场景（醉酒检测）和数据类型（面部视频）与RecSys/Search/Ads的异构数据处理需求（如用户行为序列、上下文特征）完全不同，且没有明显的技术迁移潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:34:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04536v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04536v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Alcohol consumption is a significant public health concern and a major cause of accidents and fatalities worldwide. This study introduces a novel video-based facial sequence analysis approach dedicated to the detection of alcohol intoxication. The method integrates facial landmark analysis via a Graph Attention Network (GAT) with spatiotemporal visual features extracted using a 3D ResNet. These features are dynamically fused with adaptive prioritization to enhance classification performance. Additionally, we introduce a curated dataset comprising 3,542 video segments derived from 202 individuals to support training and evaluation. Our model is compared against two baselines: a custom 3D-CNN and a VGGFace+LSTM architecture. Experimental results show that our approach achieves 95.82% accuracy, 0.977 precision, and 0.97 recall, outperforming prior methods. The findings demonstrate the model's potential for practical deployment in public safety systems for non-invasive, reliable alcohol intoxication detection.
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            <a href="https://www.alphaxiv.org/abs/2512.04534v1" target="_blank" rel="noopener noreferrer">
                Refaçade：基于给定参考纹理的物体编辑
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            Refaçade: Editing Object with Given Reference Texture
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Youze Huang, Penghui Ruan, Bojia Zi, Xianbiao Qi, Jianan Wang, Rong Xiao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的物体纹理编辑，属于纯粹的视觉处理任务。虽然可能涉及生成模型技术，但标题未表明与推荐系统、搜索或广告的明确关联，且属于“Irrelevant Topics”中明确的“Purely Vision”类别，无直接或间接的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:30:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04534v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04534v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in diffusion models have brought remarkable progress in image and video editing, yet some tasks remain underexplored. In this paper, we introduce a new task, Object Retexture, which transfers local textures from a reference object to a target object in images or videos. To perform this task, a straightforward solution is to use ControlNet conditioned on the source structure and the reference texture. However, this approach suffers from limited controllability for two reasons: conditioning on the raw reference image introduces unwanted structural information, and it fails to disentangle the visual texture and structure information of the source. To address this problem, we propose Refaçade, a method that consists of two key designs to achieve precise and controllable texture transfer in both images and videos. First, we employ a texture remover trained on paired textured/untextured 3D mesh renderings to remove appearance information while preserving the geometry and motion of source videos. Second, we disrupt the reference global layout using a jigsaw permutation, encouraging the model to focus on local texture statistics rather than the global layout of the object. Extensive experiments demonstrate superior visual quality, precise editing, and controllability, outperforming strong baselines in both quantitative and human evaluations. Code is available at https://github.com/fishZe233/Refacade.
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            <a href="https://www.alphaxiv.org/abs/2512.04528v1" target="_blank" rel="noopener noreferrer">
                Auto3R：通过数据驱动的不确定性量化实现自动化三维重建与扫描
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            Auto3R: Automated 3D Reconstruction and Scanning via Data-driven Uncertainty Quantification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chentao Shen, Sizhe Zheng, Bingqian Wu, Yaohua Feng, Yuanchen Fei, Mingyu Mei, H...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于三维重建、扫描和不确定性量化，属于计算机视觉和三维视觉领域。根据您指定的无关主题列表，明确排除了“Purely Vision、3D Vision、Graphic或Speech papers without clear relevance to RecSys/Search/Ads”。标题中未提及任何与推荐系统、搜索、广告、Transformer架构或LLM技术相关的元素，也没有暗示这些视觉技术可能应用于您的核心领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:20:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04528v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04528v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Traditional high-quality 3D scanning and reconstruction typically relies on human labor to plan the scanning procedure. With the rapid development of embodied systems such as drones and robots, there is a growing demand of performing accurate 3D scanning and reconstruction in an fully automated manner. We introduce Auto3R, a data-driven uncertainty quantification model that is designed to automate the 3D scanning and reconstruction of scenes and objects, including objects with non-lambertian and specular materials. Specifically, in a process of iterative 3D reconstruction and scanning, Auto3R can make efficient and accurate prediction of uncertainty distribution over potential scanning viewpoints, without knowing the ground truth geometry and appearance. Through extensive experiments, Auto3R achieves superior performance that outperforms the state-of-the-art methods by a large margin. We also deploy Auto3R on a robot arm equipped with a camera and demonstrate that Auto3R can be used to effectively digitize real-world 3D objects and delivers ready-to-use and photorealistic digital assets. Our homepage: https://tomatoma00.github.io/auto3r.github.io .
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            <a href="https://www.alphaxiv.org/abs/2512.04521v1" target="_blank" rel="noopener noreferrer">
                基于WiFi的跨领域手势识别注意力机制研究
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            WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruijing Liu, Cunhua Pan, Jiaming Zeng, Hong Ren, Kezhi Wang, Lei Kong, Jiangzhou...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及WiFi信号处理和手势识别，属于传感器数据处理领域，与推荐系统、搜索或广告的核心技术无直接关联。注意力机制虽为Transformer相关技术，但论文应用场景（手势识别）与指定领域（RecSys/Search/Ads）无明确应用潜力，不符合任何当前关注点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:09:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04521v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04521v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">eess.SP</span></div>
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                    While fulfilling communication tasks, wireless signals can also be used to sense the environment. Among various types of sensing media, WiFi signals offer advantages such as widespread availability, low hardware cost, and strong robustness to environmental conditions like light, temperature, and humidity. By analyzing Wi-Fi signals in the environment, it is possible to capture dynamic changes of the human body and accomplish sensing applications such as gesture recognition. Although many existing gesture sensing solutions perform well in-domain but lack cross-domain capabilities (i.e., recognition performance in untrained environments). To address this, we extract Doppler spectra from the channel state information (CSI) received by all receivers and concatenate each Doppler spectrum along the same time axis to generate fused images with multi-angle information as input features. Furthermore, inspired by the convolutional block attention module (CBAM), we propose a gesture recognition network that integrates a multi-semantic spatial attention mechanism with a self-attention-based channel mechanism. This network constructs attention maps to quantify the spatiotemporal features of gestures in images, enabling the extraction of key domain-independent features. Additionally, ResNet18 is employed as the backbone network to further capture deep-level features. To validate the network performance, we evaluate the proposed network on the public Widar3 dataset, and the results show that it not only maintains high in-domain accuracy of 99.72%, but also achieves high performance in cross-domain recognition of 97.61%, significantly outperforming existing best solutions.
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            <a href="https://www.alphaxiv.org/abs/2512.04520v1" target="_blank" rel="noopener noreferrer">
                面向零样本医学图像分割的边界感知测试时自适应
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            Boundary-Aware Test-Time Adaptation for Zero-Shot Medical Image Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenlin Xu, Lei Zhang, Lituan Wang, Xinyu Pu, Pengfei Ma, Guangwu Qian, Zizhou W...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学图像分割，这属于明确的无关主题（医学/生物学领域应用）。虽然提到了测试时自适应技术，但该技术被应用于医学图像这一特定领域，与推荐系统、搜索或广告的核心技术进展没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:08:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04520v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04520v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Due to the scarcity of annotated data and the substantial computational costs of model, conventional tuning methods in medical image segmentation face critical challenges. Current approaches to adapting pretrained models, including full-parameter and parameter-efficient fine-tuning, still rely heavily on task-specific training on downstream tasks. Therefore, zero-shot segmentation has gained increasing attention, especially with foundation models such as SAM demonstrating promising generalization capabilities. However, SAM still faces notable limitations on medical datasets due to domain shifts, making efficient zero-shot enhancement an urgent research goal. To address these challenges, we propose BA-TTA-SAM, a task-agnostic test-time adaptation framework that significantly enhances the zero-shot segmentation performance of SAM via test-time adaptation. This framework integrates two key mechanisms: (1) The encoder-level Gaussian prompt injection embeds Gaussian-based prompts directly into the image encoder, providing explicit guidance for initial representation learning. (2) The cross-layer boundary-aware attention alignment exploits the hierarchical feature interactions within the ViT backbone, aligning deep semantic responses with shallow boundary cues. Experiments on four datasets, including ISIC, Kvasir, BUSI, and REFUGE, show an average improvement of 12.4\% in the DICE score compared with SAM's zero-shot segmentation performance. The results demonstrate that our method consistently outperforms state-of-the-art models in medical image segmentation. Our framework significantly enhances the generalization ability of SAM, without requiring any source-domain training data. Extensive experiments on publicly available medical datasets strongly demonstrate the superiority of our framework. Our code is available at https://github.com/Emilychenlin/BA-TTA-SAM.
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            <a href="https://www.alphaxiv.org/abs/2512.04519v1" target="_blank" rel="noopener noreferrer">
                VideoSSM：基于混合状态空间记忆的自回归长视频生成
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            VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yifei Yu, Xiaoshan Wu, Xinting Hu, Tao Hu, Yangtian Sun, Xiaoyang Lyu, Bo Wang, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于长视频生成技术，属于纯粹的视觉内容生成领域，与推荐系统、搜索或广告的核心排名任务无关。虽然使用了状态空间模型等架构，但没有展示在异构数据处理或推荐/搜索/广告应用中的潜力，完全属于被排除的“AIGC/内容生成”类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 07:06:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04519v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04519v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Autoregressive (AR) diffusion enables streaming, interactive long-video generation by producing frames causally, yet maintaining coherence over minute-scale horizons remains challenging due to accumulated errors, motion drift, and content repetition. We approach this problem from a memory perspective, treating video synthesis as a recurrent dynamical process that requires coordinated short- and long-term context. We propose VideoSSM, a Long Video Model that unifies AR diffusion with a hybrid state-space memory. The state-space model (SSM) serves as an evolving global memory of scene dynamics across the entire sequence, while a context window provides local memory for motion cues and fine details. This hybrid design preserves global consistency without frozen, repetitive patterns, supports prompt-adaptive interaction, and scales in linear time with sequence length. Experiments on short- and long-range benchmarks demonstrate state-of-the-art temporal consistency and motion stability among autoregressive video generator especially at minute-scale horizons, enabling content diversity and interactive prompt-based control, thereby establishing a scalable, memory-aware framework for long video generation.
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            <a href="https://www.alphaxiv.org/abs/2512.04515v1" target="_blank" rel="noopener noreferrer">
                EgoLCD：基于长上下文扩散模型的自中心视频生成
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            EgoLCD: Egocentric Video Generation with Long Context Diffusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liuzhou Zhang, Jiarui Ye, Yuanlei Wang, Ming Zhong, Mingju Cao, Wanke Xia, Bowen...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自中心视频生成，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及扩散模型，但论文的应用场景（视频生成）和核心问题（自中心视角）与用户行为建模、内容排序或广告投放等RecSys/Search/Ads任务没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 06:53:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04515v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04515v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating long, coherent egocentric videos is difficult, as hand-object interactions and procedural tasks require reliable long-term memory. Existing autoregressive models suffer from content drift, where object identity and scene semantics degrade over time. To address this challenge, we introduce EgoLCD, an end-to-end framework for egocentric long-context video generation that treats long video synthesis as a problem of efficient and stable memory management. EgoLCD combines a Long-Term Sparse KV Cache for stable global context with an attention-based short-term memory, extended by LoRA for local adaptation. A Memory Regulation Loss enforces consistent memory usage, and Structured Narrative Prompting provides explicit temporal guidance. Extensive experiments on the EgoVid-5M benchmark demonstrate that EgoLCD achieves state-of-the-art performance in both perceptual quality and temporal consistency, effectively mitigating generative forgetting and representing a significant step toward building scalable world models for embodied AI. Code: https://github.com/AIGeeksGroup/EgoLCD. Website: https://aigeeksgroup.github.io/EgoLCD.
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            <a href="https://www.alphaxiv.org/abs/2512.04511v1" target="_blank" rel="noopener noreferrer">
                DuGI-MAE：通过双域引导改进红外掩码自编码器
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            DuGI-MAE: Improving Infrared Mask Autoencoders via Dual-Domain Guidance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yinghui Xing, Xiaoting Su, Shizhou Zhang, Donghao Chu, Di Xu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于红外图像处理和掩码自编码器技术，属于计算机视觉领域。虽然自编码器是生成模型的一种，但该工作专门针对红外图像这一特定模态，与推荐系统、搜索或广告中的异构数据处理没有明显关联。标题中未提及任何可能应用于RecSys/Search/Ads的通用技术或架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 06:45:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04511v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04511v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Infrared imaging plays a critical role in low-light and adverse weather conditions. However, due to the distinct characteristics of infrared images, existing foundation models such as Masked Autoencoder (MAE) trained on visible data perform suboptimal in infrared image interpretation tasks. To bridge this gap, an infrared foundation model known as InfMAE was developed and pre-trained on large-scale infrared datasets. Despite its effectiveness, InfMAE still faces several limitations, including the omission of informative tokens, insufficient modeling of global associations, and neglect of non-uniform noise. In this paper, we propose a Dual-domain Guided Infrared foundation model based on MAE (DuGI-MAE). First, we design a deterministic masking strategy based on token entropy, preserving only high-entropy tokens for reconstruction to enhance informativeness. Next, we introduce a Dual-Domain Guidance (DDG) module, which simultaneously captures global token relationships and adaptively filters non-uniform background noise commonly present in infrared imagery. To facilitate large-scale pretraining, we construct Inf-590K, a comprehensive infrared image dataset encompassing diverse scenes, various target types, and multiple spatial resolutions. Pretrained on Inf-590K, DuGI-MAE demonstrates strong generalization capabilities across various downstream tasks, including infrared object detection, semantic segmentation, and small target detection. Experimental results validate the superiority of the proposed method over both supervised and self-supervised comparison methods. Our code is available in the supplementary material.
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            <a href="https://www.alphaxiv.org/abs/2512.04504v1" target="_blank" rel="noopener noreferrer">
                UltraImage：重新思考图像扩散变换器中的分辨率外推
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            UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Min Zhao, Bokai Yan, Xue Yang, Hongzhou Zhu, Jintao Zhang, Shilong Liu, Chongxua...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像扩散变换器的分辨率外推技术，属于纯粹的计算机视觉领域研究。虽然涉及Transformer架构，但论文内容明确限定于图像生成任务，没有提及或暗示在推荐系统、搜索或广告领域的潜在应用，因此与所有关注点均不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 06:24:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04504v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04504v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent image diffusion transformers achieve high-fidelity generation, but struggle to generate images beyond these scales, suffering from content repetition and quality degradation. In this work, we present UltraImage, a principled framework that addresses both issues. Through frequency-wise analysis of positional embeddings, we identify that repetition arises from the periodicity of the dominant frequency, whose period aligns with the training resolution. We introduce a recursive dominant frequency correction to constrain it within a single period after extrapolation. Furthermore, we find that quality degradation stems from diluted attention and thus propose entropy-guided adaptive attention concentration, which assigns higher focus factors to sharpen local attention for fine detail and lower ones to global attention patterns to preserve structural consistency. Experiments show that UltraImage consistently outperforms prior methods on Qwen-Image and Flux (around 4K) across three generation scenarios, reducing repetition and improving visual fidelity. Moreover, UltraImage can generate images up to 6K*6K without low-resolution guidance from a training resolution of 1328p, demonstrating its extreme extrapolation capability. Project page is available at \href{https://thu-ml.github.io/ultraimage.github.io/}{https://thu-ml.github.io/ultraimage.github.io/}.
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            <a href="https://www.alphaxiv.org/abs/2512.04499v1" target="_blank" rel="noopener noreferrer">
                回归基础：运动表示对基于扩散模型的人体运动生成至关重要
            </a>
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            Back to Basics: Motion Representation Matters for Human Motion Generation Using Diffusion Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuduo Jin, Brandon Haworth
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人体运动生成这一计算机视觉/图形学领域，与推荐系统、搜索或广告的核心技术无直接关联。扩散模型虽属生成模型范畴，但论文的应用场景（人体运动）属于纯粹的视觉/图形领域，不符合当前关注的任何技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 06:05:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04499v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04499v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span></div>
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                    Diffusion models have emerged as a widely utilized and successful methodology in human motion synthesis. Task-oriented diffusion models have significantly advanced action-to-motion, text-to-motion, and audio-to-motion applications. In this paper, we investigate fundamental questions regarding motion representations and loss functions in a controlled study, and we enumerate the impacts of various decisions in the workflow of the generative motion diffusion model. To answer these questions, we conduct empirical studies based on a proxy motion diffusion model (MDM). We apply v loss as the prediction objective on MDM (vMDM), where v is the weighted sum of motion data and noise. We aim to enhance the understanding of latent data distributions and provide a foundation for improving the state of conditional motion diffusion models. First, we evaluate the six common motion representations in the literature and compare their performance in terms of quality and diversity metrics. Second, we compare the training time under various configurations to shed light on how to speed up the training process of motion diffusion models. Finally, we also conduct evaluation analysis on a large motion dataset. The results of our experiments indicate clear performance differences across motion representations in diverse datasets. Our results also demonstrate the impacts of distinct configurations on model training and suggest the importance and effectiveness of these decisions on the outcomes of motion diffusion models.
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            <a href="https://www.alphaxiv.org/abs/2512.04496v1" target="_blank" rel="noopener noreferrer">
                移位窗口与双重注意力相遇：用于镜面高光去除的多模型架构
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            Shift-Window Meets Dual Attention: A Multi-Model Architecture for Specular Highlight Removal
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianci Huo, Lingfeng Qi, Yuhan Chen, Qihong Xue, Jinyuan Shao, Hai Yu, Jie Li, Z...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及计算机视觉中的镜面高光去除任务，属于纯粹的视觉处理领域。虽然提到了多模型架构和注意力机制，但这些技术并未与推荐系统、搜索或广告领域建立任何直接或潜在的应用联系，完全属于不相关主题中的'Purely Vision'类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 06:02:37
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                <a href="https://arxiv.org/abs/2512.04496v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04496v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Inevitable specular highlights in practical environments severely impair the visual performance, thus degrading the task effectiveness and efficiency. Although there exist considerable methods that focus on local information from convolutional neural network models or global information from transformer models, the single-type model falls into a modeling dilemma between local fine-grained details and global long-range dependencies, thus deteriorating for specular highlights with different scales. Therefore, to accommodate specular highlights of all scales, we propose a multi-model architecture for specular highlight removal (MM-SHR) that effectively captures fine-grained features in highlight regions and models long-range dependencies between highlight and highlight-free areas. Specifically, we employ convolution operations to extract local details in the shallow layers of MM-SHR, and utilize the attention mechanism to capture global features in the deep layers, ensuring both operation efficiency and removal accuracy. To model long-range dependencies without compromising computational complexity, we utilize a coarse-to-fine manner and propose Omni-Directional Attention Integration Block(OAIBlock) and Adaptive Region-Aware Hybrid-Domain Dual Attention Convolutional Network(HDDAConv) , which leverage omni-directiona pixel-shifting and window-dividing operations at the raw features to achieve specular highlight removal. Extensive experimental results on three benchmark tasks and six types of surface materials demonstrate that MM-SHR outperforms state-of-the-art methods in both accuracy and efficiency for specular highlight removal. The implementation will be made publicly available at https://github.com/Htcicv/MM-SHR.
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            <a href="https://www.alphaxiv.org/abs/2512.04487v1" target="_blank" rel="noopener noreferrer">
                基于扩展关节目标的可控长期运动生成
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            Controllable Long-term Motion Generation with Extended Joint Targets
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Eunjong Lee, Eunhee Kim, Sanghoon Hong, Eunho Jung, Jihoon Kim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的运动生成，属于纯粹的视觉/3D视觉领域，没有明确展示与推荐系统、搜索或广告的相关性。标题中的'关节目标'和'运动生成'表明这是关于3D人体运动或动画生成的研究，属于您指定的不相关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:44:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04487v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04487v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating stable and controllable character motion in real-time is a key challenge in computer animation. Existing methods often fail to provide fine-grained control or suffer from motion degradation over long sequences, limiting their use in interactive applications. We propose COMET, an autoregressive framework that runs in real time, enabling versatile character control and robust long-horizon synthesis. Our efficient Transformer-based conditional VAE allows for precise, interactive control over arbitrary user-specified joints for tasks like goal-reaching and in-betweening from a single model. To ensure long-term temporal stability, we introduce a novel reference-guided feedback mechanism that prevents error accumulation. This mechanism also serves as a plug-and-play stylization module, enabling real-time style transfer. Extensive evaluations demonstrate that COMET robustly generates high-quality motion at real-time speeds, significantly outperforming state-of-the-art approaches in complex motion control tasks and confirming its readiness for demanding interactive applications.
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            <a href="https://www.alphaxiv.org/abs/2512.04485v1" target="_blank" rel="noopener noreferrer">
                并非所有鸟类都看起来一样：面向鸟类的身份保持生成
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            Not All Birds Look The Same: Identity-Preserving Generation For Birds
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aaron Sun, Oindrila Saha, Subhransu Maji
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于特定领域（鸟类）的生成模型，属于纯粹的视觉内容生成范畴。虽然涉及生成技术，但缺乏与推荐系统、搜索或广告领域的明确关联，且未提及任何可能应用于这些领域的通用技术或架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:39:12
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                <a href="https://arxiv.org/abs/2512.04485v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04485v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Since the advent of controllable image generation, increasingly rich modes of control have enabled greater customization and accessibility for everyday users. Zero-shot, identity-preserving models such as Insert Anything and OminiControl now support applications like virtual try-on without requiring additional fine-tuning. While these models may be fitting for humans and rigid everyday objects, they still have limitations for non-rigid or fine-grained categories. These domains often lack accessible, high-quality data -- especially videos or multi-view observations of the same subject -- making them difficult both to evaluate and to improve upon. Yet, such domains are essential for moving beyond content creation toward applications that demand accuracy and fine detail. Birds are an excellent domain for this task: they exhibit high diversity, require fine-grained cues for identification, and come in a wide variety of poses. We introduce the NABirds Look-Alikes (NABLA) dataset, consisting of 4,759 expert-curated image pairs. Together with 1,073 pairs collected from multi-image observations on iNaturalist and a small set of videos, this forms a benchmark for evaluating identity-preserving generation of birds. We show that state-of-the-art baselines fail to maintain identity on this dataset, and we demonstrate that training on images grouped by species, age, and sex -- used as a proxy for identity -- substantially improves performance on both seen and unseen species.
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            <a href="https://www.alphaxiv.org/abs/2512.04464v1" target="_blank" rel="noopener noreferrer">
                特征工程与深度学习在自动硬币评级中的对比研究：以圣高登斯双鹰金币为例
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            Feature Engineering vs. Deep Learning for Automated Coin Grading: A Comparative Study on Saint-Gaudens Double Eagles
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tanmay Dogra, Eric Ngo, Mohammad Alam, Jean-Paul Talavera, Asim Dahal
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于硬币评级这一特定领域应用，属于计算机视觉在收藏品领域的应用，与推荐系统、搜索或广告的核心领域进展、LLM技术、Transformer架构或异质数据统一建模均无直接关联。论文比较特征工程与深度学习方法，属于传统机器学习与深度学习的对比研究，没有涉及推荐/搜索/广告领域的技术或应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:13:53
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                <a href="https://arxiv.org/abs/2512.04464v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04464v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    We challenge the common belief that deep learning always trumps older techniques, using the example of grading Saint-Gaudens Double Eagle gold coins automatically. In our work, we put a feature-based Artificial Neural Network built around 192 custom features pulled from Sobel edge detection and HSV color analysis up against a hybrid Convolutional Neural Network that blends in EfficientNetV2, plus a straightforward Support Vector Machine as the control. Testing 1,785 coins graded by experts, the ANN nailed 86% exact matches and hit 98% when allowing a 3-grade leeway. On the flip side, CNN and SVM mostly just guessed the most common grade, scraping by with 31% and 30% exact hits. Sure, the CNN looked good on broader tolerance metrics, but that is because of some averaging trick in regression that hides how it totally flops at picking out specific grades. All told, when you are stuck with under 2,000 examples and lopsided classes, baking in real coin-expert knowledge through feature design beats out those inscrutable, all-in-one deep learning setups. This rings true for other niche quality checks where data's thin and know-how matters more than raw compute.
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            <a href="https://www.alphaxiv.org/abs/2512.04461v1" target="_blank" rel="noopener noreferrer">
                UniTS：面向遥感数据的统一时间序列生成模型
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            UniTS: Unified Time Series Generative Model for Remote Sensing
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxiang Zhang, Shunlin Liang, Wenyuan Li, Han Ma, Jianglei Xu, Yichuan Ma, Jiang...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感领域的时间序列生成，属于特定领域应用（遥感/地球科学），与推荐系统、搜索或广告的核心关注点无直接关联。虽然时间序列建模在推荐系统中可能用于用户行为序列分析，但该论文明确限定于遥感数据，缺乏明确的跨领域应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:07:35
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                <a href="https://arxiv.org/abs/2512.04461v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04461v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    One of the primary objectives of satellite remote sensing is to capture the complex dynamics of the Earth environment, which encompasses tasks such as reconstructing continuous cloud-free time series images, detecting land cover changes, and forecasting future surface evolution. However, existing methods typically require specialized models tailored to different tasks, lacking unified modeling of spatiotemporal features across multiple time series tasks. In this paper, we propose a Unified Time Series Generative Model (UniTS), a general framework applicable to various time series tasks, including time series reconstruction, time series cloud removal, time series semantic change detection, and time series forecasting. Based on the flow matching generative paradigm, UniTS constructs a deterministic evolution path from noise to targets under the guidance of task-specific conditions, achieving unified modeling of spatiotemporal representations for multiple tasks. The UniTS architecture consists of a diffusion transformer with spatio-temporal blocks, where we design an Adaptive Condition Injector (ACor) to enhance the model's conditional perception of multimodal inputs, enabling high-quality controllable generation. Additionally, we design a Spatiotemporal-aware Modulator (STM) to improve the ability of spatio-temporal blocks to capture complex spatiotemporal dependencies. Furthermore, we construct two high-quality multimodal time series datasets, TS-S12 and TS-S12CR, filling the gap of benchmark datasets for time series cloud removal and forecasting tasks. Extensive experiments demonstrate that UniTS exhibits exceptional generative and cognitive capabilities in both low-level and high-level time series tasks. It significantly outperforms existing methods, particularly when facing challenges such as severe cloud contamination, modality absence, and forecasting phenological variations.
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            <a href="https://www.alphaxiv.org/abs/2512.04459v1" target="_blank" rel="noopener noreferrer">
                dVLM-AD：通过可控推理增强用于驾驶的扩散视觉语言模型
            </a>
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            dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yingzi Ma, Yulong Cao, Wenhao Ding, Shuibai Zhang, Yan Wang, Boris Ivanovic, Min...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的扩散视觉语言模型，属于纯粹的视觉应用。虽然涉及多模态建模，但自动驾驶与推荐系统、搜索或广告领域没有直接关联。论文没有展示在异构数据处理或推荐/搜索应用方面的潜力，因此与当前关注点无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:05:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04459v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04459v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The autonomous driving community is increasingly focused on addressing the challenges posed by out-of-distribution (OOD) driving scenarios. A dominant research trend seeks to enhance end-to-end (E2E) driving systems by integrating vision-language models (VLMs), leveraging their rich world knowledge and reasoning abilities to improve generalization across diverse environments. However, most existing VLMs or vision-language agents (VLAs) are built upon autoregressive (AR) models. In this paper, we observe that existing AR-based VLMs -- limited by causal attention and sequential token generation -- often fail to maintain consistency and controllability between high-level reasoning and low-level planning. In contrast, recent discrete diffusion VLMs equipped with bidirectional attention exhibit superior controllability and reliability through iterative denoising. Building on these observations, we introduce dVLM-AD, a diffusion-based vision-language model that unifies perception, structured reasoning, and low-level planning for end-to-end driving. Evaluated on nuScenes and WOD-E2E, dVLM-AD yields more consistent reasoning-action pairs and achieves planning performance comparable to existing driving VLM/VLA systems despite a modest backbone, outperforming AR-based baselines with a 9 percent improvement in behavior-trajectory consistency and a 6 percent increase in RFS on long-tail WOD-E2E scenarios. These results suggest a controllable and reliable pathway for scalable end-to-end driving.
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 * @Date: 2025-10-09 23:23:38
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            <a href="https://www.alphaxiv.org/abs/2512.04456v1" target="_blank" rel="noopener noreferrer">
                GuidNoise：用于广义噪声合成的单对引导扩散方法
            </a>
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        <div class="mb-2 text-base text-gray-700">
            GuidNoise: Single-Pair Guided Diffusion for Generalized Noise Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Changjin Kim, HyeokJun Lee, YoungJoon Yoo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于扩散模型在噪声合成领域的特定应用，属于生成式AI的范畴。虽然扩散模型是LLM/Transformer相关技术的一部分，但该论文明确针对噪声合成这一狭窄应用，与推荐系统、搜索或广告中的核心问题（如排序、匹配、用户建模）缺乏直接关联，也不涉及异构数据处理或多模态建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 05:00:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04456v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04456v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent image denoising methods have leveraged generative modeling for real noise synthesis to address the costly acquisition of real-world noisy data. However, these generative models typically require camera metadata and extensive target-specific noisy-clean image pairs, often showing limited generalization between settings. In this paper, to mitigate the prerequisites, we propose a Single-Pair Guided Diffusion for generalized noise synthesis GuidNoise, which uses a single noisy/clean pair as the guidance, often easily obtained by itself within a training set. To train GuidNoise, which generates synthetic noisy images from the guidance, we introduce a guidance-aware affine feature modification (GAFM) and a noise-aware refine loss to leverage the inherent potential of diffusion models. This loss function refines the diffusion model's backward process, making the model more adept at generating realistic noise distributions. The GuidNoise synthesizes high-quality noisy images under diverse noise environments without additional metadata during both training and inference. Additionally, GuidNoise enables the efficient generation of noisy-clean image pairs at inference time, making synthetic noise readily applicable for augmenting training data. This self-augmentation significantly improves denoising performance, especially in practical scenarios with lightweight models and limited training data. The code is available at https://github.com/chjinny/GuidNoise.
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            <a href="https://www.alphaxiv.org/abs/2512.04451v1" target="_blank" rel="noopener noreferrer">
                StreamEQA：面向具身场景的流式视频理解
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        <div class="mb-2 text-base text-gray-700">
            StreamEQA: Towards Streaming Video Understanding for Embodied Scenarios
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yifei Wang, Zhenkai Li, Tianwen Qian, Huanran Zheng, Zheng Wang, Yuqian Fu, Xiao...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于具身场景的流式视频理解，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术焦点无关。标题中没有提到任何与Transformer架构、LLM技术、多模态建模或推荐/搜索/广告应用相关的内容，因此与您关注的所有技术方向均不匹配。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 04:48:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04451v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04451v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As embodied intelligence advances toward real-world deployment, the ability to continuously perceive and reason over streaming visual inputs becomes essential. In such settings, an agent must maintain situational awareness of its environment, comprehend the interactions with surrounding entities, and dynamically plan actions informed by past observations, current contexts, and anticipated future events. To facilitate progress in this direction, we introduce StreamEQA, the first benchmark designed for streaming video question answering in embodied scenarios. StreamEQA evaluates existing MLLMs along two orthogonal dimensions: Embodied and Streaming. Along the embodied dimension, we categorize the questions into three levels: perception, interaction, and planning, which progressively assess a model's ability to recognize fine-grained visual details, reason about agent-object interactions, and perform high-level goal-directed reasoning. For the streaming dimension, questions are divided into backward, real-time, and forward reasoning, with each mode relying on a distinct temporal context. Built upon 156 independent long videos, StreamEQA defines 42 tasks and generates approximately 21K question-answer pairs with precise timestamps through a hybrid pipeline combining automated generation and human refinement. Evaluations of 13 state-of-the-art video-LLMs reveal that, despite strong performance on conventional benchmarks, these models still struggle with streaming video understanding in embodied scenarios. We hope StreamEQA will catalyze research on streaming video understanding for embodied applications.
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            <a href="https://www.alphaxiv.org/abs/2512.04441v1" target="_blank" rel="noopener noreferrer">
                MindDrive：一个连接世界模型与视觉语言模型用于端到端自动驾驶的一体化框架
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            MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bin Suna, Yaoguang Caob, Yan Wanga, Rui Wanga, Jiachen Shanga, Xiejie Fenga, Jia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于自动驾驶领域，属于明确的无关主题（自动驾驶属于物理/特定领域应用）。虽然提到了视觉语言模型，但应用场景是自动驾驶而非推荐系统、搜索或广告。没有显示任何与推荐系统、搜索或广告相关的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-04 04:16:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04441v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04441v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    End-to-End autonomous driving (E2E-AD) has emerged as a new paradigm, where trajectory planning plays a crucial role. Existing studies mainly follow two directions: trajectory generation oriented, which focuses on producing high-quality trajectories with simple decision mechanisms, and trajectory selection oriented, which performs multi-dimensional evaluation to select the best trajectory yet lacks sufficient generative capability. In this work, we propose MindDrive, a harmonized framework that integrates high-quality trajectory generation with comprehensive decision reasoning. It establishes a structured reasoning paradigm of "context simulation - candidate generation - multi-objective trade-off". In particular, the proposed Future-aware Trajectory Generator (FaTG), based on a World Action Model (WaM), performs ego-conditioned "what-if" simulations to predict potential future scenes and generate foresighted trajectory candidates. Building upon this, the VLM-oriented Evaluator (VLoE) leverages the reasoning capability of a large vision-language model to conduct multi-objective evaluations across safety, comfort, and efficiency dimensions, leading to reasoned and human-aligned decision making. Extensive experiments on the NAVSIM-v1 and NAVSIM-v2 benchmarks demonstrate that MindDrive achieves state-of-the-art performance across multi-dimensional driving metrics, significantly enhancing safety, compliance, and generalization. This work provides a promising path toward interpretable and cognitively guided autonomous driving.
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                    document.getElementById('show-all').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 隐藏展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) expandToggle.style.display = 'none';
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'none';
                });
            }
            
            // 实现"全部论文"按钮功能
            const showAllButton = document.getElementById('show-all');
            if (showAllButton) {
                showAllButton.addEventListener('click', function() {
                    // 显示所有论文
                    const allPapers = document.querySelectorAll('.paper-card, .simple-paper-card');
                    allPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    // 重置折叠状态
                    papersContainer.classList.remove('expanded-all');
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${allPapers.length} 篇论文 (共 ${allPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-selected').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 重新显示展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) {
                        expandToggle.style.display = 'block';
                        expandToggle.textContent = '展开全部非精选论文';
                    }
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'block';
                });
            }
        });
    </script>
    <script>
    
    // 初始化日历
    document.addEventListener('DOMContentLoaded', () => {
        try {
            console.log('Attempting to initialize calendar...');
            initCalendar();
        } catch (error) {
            console.error('Error initializing calendar:', error);
        }
    });
    
    // 日历初始化函数
    function initCalendar() {
        const toggleBtn = document.getElementById('date-picker-toggle');
        const datePicker = document.getElementById('date-picker');
        const calendarGrid = document.getElementById('calendar-grid');
        const prevMonthBtn = document.getElementById('prev-month');
        const nextMonthBtn = document.getElementById('next-month');
        const currentMonthEl = document.getElementById('current-month');
        const selectedDateText = document.getElementById('selected-date-text');
        
        // 当前显示的日期（从页面获取）
        const currentDateStr = document.getElementById('current-date').textContent.trim().replace(/^\d+年|月|日/g, '');
        const currentDate = new Date(currentDateStr);
        let displayYear = currentDate.getFullYear();
        let displayMonth = currentDate.getMonth();
        
        // 有论文数据的日期列表
        const availableDates = ["20251105","20251107","20251009","20251121","20251113","20251030","20251111","20251126","20251204","20251031","20251017","20251021","20251010","20251202","20251127","20251024","20251022","20251029","20251114","20251118","20251120","20251016","20251015","20251028","20251014","20251119","20251112","20251106","20251125","20251205","20251023"];
        
        // 尝试从localStorage恢复选择状态
        const savedDate = localStorage.getItem('selectedDate');
        const savedYear = localStorage.getItem('selectedYear');
        const savedMonth = localStorage.getItem('selectedMonth');
        
        // 确保页面加载时显示当前选中的日期
        // 修复持久化问题：确保每次加载都能正确恢复选中状态
        if (savedDate) {
            selectedDateText.textContent = savedDate;
            if (savedYear) displayYear = parseInt(savedYear);
            if (savedMonth) displayMonth = parseInt(savedMonth);
        } else {
            // 首次加载时，将当前页面日期保存到localStorage
            const currentPageDate = currentDateStr.replace(/\//g, '-');
            selectedDateText.textContent = currentPageDate;
            localStorage.setItem('selectedDate', currentPageDate);
            localStorage.setItem('selectedYear', currentDate.getFullYear().toString());
            localStorage.setItem('selectedMonth', currentDate.getMonth().toString());
        }
    
        // 切换日历显示状态
        toggleBtn.addEventListener('click', (e) => {
            e.stopPropagation();
            
            // 显式控制hidden类的添加和移除
            if (datePicker.classList.contains('hidden')) {
                // 显示日历 - 确保移除hidden类
                datePicker.classList.remove('hidden');
                renderCalendar();
            } else {
                // 隐藏日历
                datePicker.classList.add('hidden');
            }
        });
        
        // 点击其他区域关闭日历
        document.addEventListener('click', () => {
            if (!datePicker.classList.contains('hidden')) {
                datePicker.classList.add('hidden');
            }
        });
        
        // 阻止日历内部点击事件冒泡
        datePicker.addEventListener('click', (e) => {
            e.stopPropagation();
        });
        
        // 上月和下月按钮
        prevMonthBtn.addEventListener('click', () => {
            displayMonth--;
            if (displayMonth < 0) {
                displayMonth = 11;
                displayYear--;
            }
            renderCalendar();
        });
        
        nextMonthBtn.addEventListener('click', () => {
            displayMonth++;
            if (displayMonth > 11) {
                displayMonth = 0;
                displayYear++;
            }
            renderCalendar();
        });
        
        /**
         * 渲染日历
         */
        function renderCalendar() {
            // 清空日历网格
            calendarGrid.innerHTML = '';
            
            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>